Methods, systems, and computer program products are provided for analyzing and/or improving transformer models. A method may include receiving a trained transformer model. The trained transformer model may include at least one multi-head self-attention layer including a plurality of attention heads. At least one sample may be received. The sample(s) may be inputted to the trained transformer model to generate at least one layer output of the multi-head self-attention layer(s) and at least one model output of the trained transformer model. Each respective attention head may be pruned, and the sample(s) may be inputted to the trained transformer model with the respective attention head pruned to generate at least one respective pruned layer output and at least one respective pruned model output. At least one importance metric may be determined for each respective attention head based on at least two of the aforementioned outputs.
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
Provided are methods for identifying sub-merchants within a Global Merchant Repository (GMR) that include receiving sub-merchant data associated with one or more sub-merchants, from one or more payment facilitators associated with a plurality of sub-merchants, where the sub-merchant data comprises a Card Acceptor Identifier (CAID) associated with each of the one or more payment facilitators, assigning a plurality of pseudo acquirer identifiers to the sub-merchant data received from each of the one or more payment facilitators, and identifying the one or more sub-merchants in the GMR by mapping the sub-merchant data with transaction data associated with a plurality of merchants in the GMR, based on the plurality of pseudo acquirer identifiers and the CAID associated with the respective payment facilitators. Systems and computer program products are also disclosed.
A computer-implemented method of dispositioning a transaction associated with a payment account is disclosed herein. The method can include receiving a registration request for a computing device to host transactions associated with the payment account, generating a unique identifier for the computing device, correlating the payment account to the generated unique identifier for the computing device, receiving a transaction authorization request associated with the payment account, and routing the received transaction authorization request associated with the payment account to the computing device for dispositioning based on the correlation. The computing device can host funds associated with the payment account and wherein computing device can be configured to disposition the transaction authorization request in accordance with a predetermined rule.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/12 - Architectures de paiement spécialement adaptées aux systèmes de commerce électronique
4.
METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR PROVIDING A FRAMEWORK TO IMPROVE DISCRIMINATION OF GRAPH FEATURES BY A GRAPH NEURAL NETWORK
Provided are methods for enhancing a distribution of graph feature embeddings in an embedding space to improve discrimination of graph features by a graph neural network (GNN) that may include receiving a dataset comprising graph data associated with a graph, calculating a distance between a first set of node embeddings and a second set of node embeddings, determining a measure of uniformity for the dataset, determining a plurality of groups of node embeddings, determining a measure of alignment for the plurality of groups of node embeddings, generating a set of graph features based on the measure of uniformity, the measure of alignment, and the distance, and training the GNN based on the set of graph features to provide a trained GNN. Systems and computer program products are also disclosed.
Provided are systems that include at least one processor to receive a dataset comprising a set of labeled anomaly nodes, a set of unlabeled anomaly nodes, and a set of normal nodes, randomly sample a node to provide a set of randomly sampled nodes, generate a plurality of new nodes based on the set of labeled anomaly nodes and the set of randomly sampled nodes, combine the plurality of new nodes with the set of labeled anomaly nodes to provide a combined set of labeled anomaly nodes, and train a machine learning model based on an embedding of each labeled anomaly node in the combined set of labeled anomaly nodes, a center of the combined set of labeled anomaly nodes in an embedding space, and a center of the set of normal nodes in the embedding space. Methods and computer program products are also disclosed.
G06F 18/2323 - Techniques non hiérarchiques basées sur la théorie des graphes, p.ex. les arbres couvrants de poids minimal [MST] ou les coupes de graphes
Provided are methods for generating a multitask machine learning model based on time series data, that may include receiving input time series data associated with an input time series of data points, calculating a pairwise distance between the input time series and a plurality of time series templates, providing the pairwise distance as a first input to a building block of a residual neural network, where the residual neural network has a plurality of multi-dimensional convolutional layers; generating a first output of the first building block of the residual neural network based on the first input, generating a final output of the residual neural network based on the first output, and generating a first output of a multitask machine learning model using a first output layer and a second output of the multitask machine learning model using a second output layer. Systems and computer program products are also disclosed.
A method is disclosed. The method includes receiving, from a client device, a checkout request for a transaction between a user operating the client device and a resource provider operating the resource provider computer. The resource provider computer and the client device communicate via a first communication channel. The method includes obtaining a first one-time code, displaying, the first one-time code to the user on the client device, and determining an indication that the first one-time code matches a second one-time code that was provided by the user through a second communication channel that is different than the first communication channel. The method includes allowing the transaction to continue based on the determination that the first one-time code matches the second one-time code.
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/16 - Paiements effectués par le biais de systèmes de télécommunication
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
e.g.e.g., payment cards, identification cards, driver's license cards) that are removably attachable to a portable electronic device. In some aspects, the portable electronic device may include a charging coil, a ferromagnetic component disposed about the charging coil, and a ferromagnetic alignment component. The card can include a substrate and a magnet embedded the substrate. The magnet can magnetically couple to the ferromagnetic component disposed about the charging coil.
G06K 19/07 - Supports d'enregistrement avec des marques conductrices, des circuits imprimés ou des éléments de circuit à semi-conducteurs, p.ex. cartes d'identité ou cartes de crédit avec des puces à circuit intégré
G06K 19/077 - Supports d'enregistrement avec des marques conductrices, des circuits imprimés ou des éléments de circuit à semi-conducteurs, p.ex. cartes d'identité ou cartes de crédit avec des puces à circuit intégré - Détails de structure, p.ex. montage de circuits dans le support
A method is disclosed. The method includes receiving, by a processing computer, a payout validate message for a transaction from an aggregator computer, which receives a payout inquiry message from an originator computer of a plurality of originator computers. The aggregator computer is in communication with the plurality of originator computers. The payout inquiry message and the payout validate message can comprise a transaction amount for the transaction. The processing computer validates the payout validate message, and then transmits a payout validate response message to the aggregator computer. The payout validate response message comprises data regarding validation of the payout validate message. After transmitting the payout validate response message to the aggregator computer, the method includes receiving, by the processing computer, from the aggregator computer, a payout message. The method also includes transmitting, by the processing computer, a payout response message to the aggregator computer.
Automated computer systems and methods to determine a sentiment of information in digital information or content are disclosed. One aspect includes deriving, by a processor, the digital information from a source; generating, by the processor, a domain-specific machine learning sentiment score, based on the digital information, by one model of at least two machine learning models; autonomously mapping, by the processor, a non-domain specific knowledge graph of associations between elements in a set of digital contextual information; receiving, by the processor, sentiment graphs, each sentiment graph defining a sentiment; generating, by the processor, a graph sentiment score based on the non-domain specific knowledge graph and the sentiment graphs; generating, by the processor, a final sentiment score based on the graph sentiment score and the domain-specific machine learning sentiment score; and determining the sentiment of the information in the digital information or content via the final sentiment score.
Systems and methods for entity linking using a graph neural network are disclosed. In one aspect, a method for entity linking can include extracting a first attribute set of an unknown entity from an information source and retrieving second attribute sets of known entities from a database, wherein each of the second attribute sets corresponds to one of the known entities. The method can further include generating an unknown entity graph based on the first attribute set, generating known entity graphs based on the second attribute sets, generating an unknown entity graph embedding by applying the unknown entity graph to a graph neural network, and generating known entity graph embeddings by applying the known entity graphs to the graph neural network. The method can further include assigning the information source to one of the known entities based on the unknown entity graph embedding and the known entity graph embeddings.
G06F 16/901 - Indexation; Structures de données à cet effet; Structures de stockage
G06F 16/904 - Navigation; Visualisation à cet effet
G06F 16/907 - Recherche caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
A method for personalizing a transaction via a blockchain network via payment network is disclosed herein. The method can include receiving an application programming interface ("API") call from a computing device of a user, wherein the API call includes a private key, a public key, and a unique identifier, detecting, a non-fungible token ("NFT") on the blockchain network based on the public key, authenticating the NFT asset on the blockchain network based on the private key, storing an NFT asset associated with the NFT, the private key, the public key, and the unique identifier upon authenticating the NFT, associating the unique identifier with the NFT upon authenticating the NFT, and transmitting the NFT asset to the computing device of the user, wherein the NFT asset is configured for use in association with a transaction associated with the unique identifier.
H04L 9/00 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
Entity linking using a graph neural network is disclosed. Entity linking can include tokenizing an unknown name, tokenizing a known name from a set of known names, identifying a candidate from the set of known names, and generating a tripartite graph. The tripartite graph can include a first layer node corresponding to the unknown name, second layer nodes corresponding to words of the known name and the candidate, and a third layer node corresponding to the candidate. The method can further include assigning the unknown name to one of the known names by applying the tripartite graph to a graph neural network model.
Systems, methods, and computer program products are provided for determining influence of a node of a graph on a graph neural network (GNN). The method includes receiving a dataset including graph data associated with a graph. The method may further include selecting a target node of a plurality of nodes based on the graph data and determining target node data associated with the target node and target edge data associated with the target node. The method may further include removing the target node data and the target edge data from the dataset to provide a target graph dataset; determining a measure of influence of the target node on a GNN based on the target graph dataset, wherein the GNN was trained using the dataset; and performing an action based on the measure of influence of the target node on the GNN.
A method is disclosed. It includes prompting, by a second user device operated by a second user, a first user to interact a portable device of the first user with the second user device in a transaction and then receiving interaction data comprising a credential or token, from the portable device in a contactless communication. The method also includes determining that the transaction cannot be completed without further interaction by the first user, and responsive to determining that the transaction cannot be completed, providing at least one alternate transaction option for the first user. The method also includes receiving, from the first user, a selection of an alternate transaction option from the at least one alternate transaction option, and processing the transaction according to the selected alternate transaction option.
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p.ex. cartes à puces ou cartes magnétiques
G06Q 20/02 - Architectures, schémas ou protocoles de paiement impliquant un tiers neutre, p.ex. une autorité de certification, un notaire ou un tiers de confiance
Methods and systems for anonymizing time-series data are disclosed. An anonymizing computer can generate an anonymized sequence of time-series data that can share many useful properties, patterns, or characteristics with a private sequence of time-series data, without revealing sensitive or private information about the private sequence of time-series data. This may enable data researchers and scientists to study the anonymized sequence of time-series data in lieu of the private sequence of time-series data, thereby preserving the privacy of data subjects (e.g., people) corresponding to the private sequence of time-series data. The anonymized sequence of time-series data can be generated using an iterative optimization process that can involve updating the anonymized sequence of time-series data to minimize a loss value. The loss value can correspond to both the utility and privacy of the anonymized sequence of time-series data.
Provided are systems, methods, and computer program products for segmenting a master non-fungible token (NFT). The method includes minting an NFT on a blockchain network, segmenting the master NFT into a plurality of segments, each segment of the plurality of segments corresponding to at least one asset of a plurality of assets on the blockchain network, issuing assets of the plurality of assets to a plurality of users based on the plurality of users conducting eligible transactions, validating a request from a user of the plurality of users based on determining that the user has possession of a set of assets of the plurality of assets corresponding to the plurality of segments, and in response to validating the request, transferring the master NFT to the user via the blockchain network.
G06Q 30/0226 - Systèmes d’incitation à un usage fréquent, p.ex. programmes de miles pour voyageurs fréquents ou systèmes de points
G06Q 20/06 - Circuits privés de paiement, p.ex. impliquant de la monnaie électronique utilisée uniquement entre les participants à un programme commun de paiement
Various systems and methods of anonymously conducting a secured payment transaction between a consumer and a merchant are disclosed. The methods can be carried out at a transaction code computer in communication with an alias directory. According to the method a transaction code computer receives a request for a dynamic transaction code from a merchant computer. The request includes a merchant alias identifier. The transaction code computer queries an alias directory storing merchant information details. The transaction code computer validates the merchant with the alias directory based on the merchant alias identifier. The transaction code computer generates the dynamic transaction code and transmits a response to the request for the dynamic transaction code to the merchant computer.
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p.ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
G06K 19/06 - Supports d'enregistrement pour utilisation avec des machines et avec au moins une partie prévue pour supporter des marques numériques caractérisés par le genre de marque numérique, p.ex. forme, nature, code
20.
SYSTEM AND METHOD FOR PERFORMING DEVICE ISOLATION IN AN AUTHENTICATION NETWORK
In some embodiments, a method includes monitoring behavior patterns of a plurality of devices associated with a user in an authentication network; generating a behavioral accuracy score for each device of the plurality of devices in the authentication network based on the behavior patterns of each device of the plurality of devices; generating a deviation score for each device of the plurality of devices based on a deviation in behavior of each device of the plurality of devices from conventional device behavior; and using the behavioral accuracy score and the deviation score to determine whether to isolate a device of the plurality of devices from the authentication network. In some embodiments, the method further includes determining whether the behavioral accuracy score of a first device of the plurality of devices is within a first behavioral accuracy score category, a second behavioral accuracy score category, or a third behavioral accuracy score category.
G06F 21/71 - Protection de composants spécifiques internes ou périphériques, où la protection d'un composant mène à la protection de tout le calculateur pour assurer la sécurité du calcul ou du traitement de l’information
G06N 5/04 - Modèles d’inférence ou de raisonnement
21.
ISOLATING APPLICATION AND SOFTWARE DEVELOPMENT KIT SANDBOXES FOR SECURITY PROTECTION
Systems and methods are disclosed for application run-time architectures that provide continuous and autonomous security protection from unauthorized access to sensitive data. Several aspects comprise running, on a client device, a software development kit (SDK) in a first application sandbox with a first unique identifier (UID); and running, on the client device, an application comprising an SDK interface in a second application sandbox with a second UID, the application communicating with the SDK via the SDK interface on a runtime service. The first UID and the second UID are each associated with their own resources. The resources may include files, keys, and registries. The first application sandbox may prevent access to resources associated with the first UID by applications without the first UID. The second application sandbox may prevent access to resources associated to the second UID by applications without the second UID.
G06F 21/53 - Contrôle des usagers, programmes ou dispositifs de préservation de l’intégrité des plates-formes, p.ex. des processeurs, des micrologiciels ou des systèmes d’exploitation au stade de l’exécution du programme, p.ex. intégrité de la pile, débordement de tampon ou prévention d'effacement involontaire de données par exécution dans un environnement restreint, p.ex. "boîte à sable" ou machine virtuelle sécurisée
Methods, systems, and computer program products may formulate an iterative data mix up problem into a Markov decision process (MDP) with a tailored reward signal to guide a learning process. To solve the MDP, a deep deterministic actor-critic framework may be modified to adapt a discrete-continuous decision space for training a data augmentation policy.
An authorization data can be captured and reused for an unauthorized purpose or context during the validity period by an adversity. Current anti-replay solutions are complex and unpractical. For example, conditional access anti-replay solution requires supplementary context or behavior control services to protect against replay. However, any authorization data can be issued with an authentication timecode, which is valid during a period of short time and is non-predictable, i.e., it can be stolen but not replayed. Therefore, a timecode can be issued with the authorization data to protect against a replay attack.
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
Provided is a system for implementing a communication interface layer for managing online services, the system including at least one processor programmed or configured to receive a request for an authentication token for access to an online service, where the request includes a user identifier, generate an authentication token associated with the user identifier, transmit the authentication token to a user device, receive a request to access the online service to perform an action associated with the account via a first function of a communication interface layer, determine that the user device is authenticated for access to the online service, and perform an action involving the online service via a second function of the communication interface layer. Methods and computer program products are also provided.
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p.ex. par clés ou règles de contrôle de l’accès
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06F 21/44 - Authentification de programme ou de dispositif
G06Q 20/02 - Architectures, schémas ou protocoles de paiement impliquant un tiers neutre, p.ex. une autorité de certification, un notaire ou un tiers de confiance
G06Q 20/42 - Confirmation, p.ex. contrôle ou autorisation de paiement par le débiteur légal
G06Q 40/02 - Opérations bancaires, p.ex. calcul d'intérêts ou tenue de compte
H04W 12/069 - Authentification utilisant des certificats ou des clés pré-partagées
H04W 12/082 - Sécurité d'accès utilisant la révocation d’autorisation
H04W 12/37 - Gestion des politiques de sécurité pour des dispositifs mobiles ou pour le contrôle d’applications mobiles
25.
METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR CRYPTOGRAM-BASED TRANSACTIONS
A computer-implemented method may include: transmitting a public key to a merchant system, the public key of a payment device provider system; receiving a request for a prepaid amount from a user device of a user; in response to receiving the request, generating a cryptogram based on a payment device of the user, the prepaid amount, and a private key corresponding to the public key of the payment device provider system, the public key and the private key forming a public-private key pair associated with the payment device provider system; and transmitting the cryptogram to the user device, the cryptogram configured to authenticate the user device during an electronic payment transaction initiated by the user device with a merchant system.
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
H04L 9/14 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité utilisant plusieurs clés ou algorithmes
G06Q 20/12 - Architectures de paiement spécialement adaptées aux systèmes de commerce électronique
An embodiment includes a node receiving one or more blocks of a blockchain. The node comprising a data storage can store, in the data storage, a plurality of sets of keys and data values associated with keys of the plurality of sets of keys, the data values being data associated with the blockchain. The node can perform a validation process for the one or more blocks. The validation process includes for each of the one or more blocks a) identifying a set of keys associated with the block, b) retrieving data values associated the identified keys from the data storage, c) storing the retrieved data values into volatile memory, and d) validating the block using the data values in the volatile memory. The node can then complete the validation of the one or more blocks.
H04L 9/00 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
G06F 12/123 - Commande de remplacement utilisant des algorithmes de remplacement avec listes d’âge, p.ex. file d’attente, liste du type le plus récemment utilisé [MRU] ou liste du type le moins récemment utilisé [LRU]
A server computer may receive an authentication data packet including authentication data from a relying party computer in communication with an authenticator associated with a user device. The server computer may verify the authentication data in the authentication data packet. The server computer may store the authentication data packet in a database. The server computer may transmit to an authorizing entity computer, a data packet including data relating to the verification of the authentication data.
A computer-implemented method includes: storing, by a payment application, payment device data including: first and second payment device identifiers and first and second wallet identifiers corresponding to first and second electronic wallets in which first and second payment device credentials of first and second payment devices are respectively stored; initiating a payment transaction with a merchant system; displaying the first and second payment device identifiers; receiving a user input from the user of the user device, the user input comprising a selection of the first payment device identifier; determining that the first electronic wallet includes the first payment device credentials of the first payment device; and facilitating a transfer of payment data between the merchant system and the first electronic wallet to initiate processing of the payment transaction.
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
G06Q 20/02 - Architectures, schémas ou protocoles de paiement impliquant un tiers neutre, p.ex. une autorité de certification, un notaire ou un tiers de confiance
G06Q 20/12 - Architectures de paiement spécialement adaptées aux systèmes de commerce électronique
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
Embodiments allow for a first transfer application executing on a first user device to send funds to a second transfer application executing on a second user device along with supplemental data. The supplemental data includes one or more of a text message, an audio content, a video content, a drawing, a photograph, a multimedia file, and the like. The application provider server of the first transfer application and/or the application provider server of the second transfer application does not support transfer of the supplemental data. A processing computer positioned between the two transfer applications facilitates the transfer of supplemental data. When the funds transfer is processed by an authorizing entity, the processing computer generates an enhanced notification including the supplemental data, and transmits the enhanced notification directly to the second transfer application.
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de banque à domicile
G06Q 20/02 - Architectures, schémas ou protocoles de paiement impliquant un tiers neutre, p.ex. une autorité de certification, un notaire ou un tiers de confiance
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
A payment instrument may include a memory, a contactless chip, a sensor, a visual output component, and/or a processor. The memory may store gesture data associated with one or more predefined gestures. The contactless chip may detect an operating field of an access device and establish a communication with the access device through a near-field communication protocol. The sensor may capture further gesture data associated with a gesture made by a user with the payment instrument in the operating field of the access device. The visual output component may provide a visual output to the user. The processor may compare the further gesture data to the gesture data to determine whether a captured gesture matches a predefined gesture, and in response to the captured gesture not matching a predefined gesture, increment a counter. The processor may control, based on a current count of the counter, the visual output component.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
A token processing server computer to process tokens for non-fungible tokens is disclosed. The token processing server computer includes a processor and a memory coupled to the processor. The memory stores machine executable instructions that when executed by the processor cause the processor to issue a token identification (ID) based on a non-fungible token (NFT) and authenticate ownership of the NFT using payment rails.
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04L 9/00 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité
A method performed by a computer is disclosed. The method includes receiving a dataset including one or more data fields. The computer can then receive instructions to compute an aggregate feature of the dataset. The computer can then determine an operator and a window based on the aggregate feature to be computed. The method may then include the computer processing the dataset. The compute may then apply the operator with the window to the processed dataset to compute the aggregate feature of the dataset.
A computer-implemented method for debiasing vectorized language representations can include identifying two (or more) pairs of concepts for which debiasing is desired, computing a mean vector for each concept, determining a center point for a rotation operation to orthogonalize based on the mean vectors, and shifting the vectors to the center point before performing a rectification operation (which can be a graded rotation), after which the vectors can be shifted back from the center point. If desired, the process can be performed iteratively.
G06F 18/2132 - Extraction de caractéristiques, p.ex. en transformant l'espace des caractéristiques; Synthétisations; Mappages, p.ex. procédés de sous-espace basée sur des critères de discrimination, p.ex. l'analyse discriminante
G06F 18/2131 - Extraction de caractéristiques, p.ex. en transformant l'espace des caractéristiques; Synthétisations; Mappages, p.ex. procédés de sous-espace basée sur un traitement dans le domaine de transformation, p.ex. transformée en ondelettes
G06F 18/21 - Conception ou mise en place de systèmes ou de techniques; Extraction de caractéristiques dans l'espace des caractéristiques; Séparation aveugle de sources
G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”
G06F 18/22 - Critères d'appariement, p.ex. mesures de proximité
G06F 18/241 - Techniques de classification relatives au modèle de classification, p.ex. approches paramétriques ou non paramétriques
Provided is a system, method, and computer program product for preventing MEV attacks in a blockchain network. The system includes at least one processor programmed or configured to communicate a plurality of digests to each mining node of a plurality of mining nodes in a blockchain network, each digest of the plurality of digests generated based on a transaction request including transaction data without including the transaction data in each digest, receive, from at least one mining node of the plurality of mining nodes, block data generated based on a proof- of-work protocol and at least a portion of digests of the plurality of digests, request the transaction data for each transaction request of a plurality of transaction requests corresponding to the at least a portion of digests, and publish a new block to the blockchain network based on the transaction data and the block data.
H04L 9/30 - Clé publique, c. à d. l'algorithme de chiffrement étant impossible à inverser par ordinateur et les clés de chiffrement des utilisateurs n'exigeant pas le secret
H04L 9/00 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité
H04L 9/06 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p.ex. système DES
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p.ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
35.
DISTRIBUTED EXECUTION OF A MACHINE-LEARNING MODEL ON A SERVER CLUSTER
Described are a system, method, and computer program product for distributed execution of a machine-learning model on a server cluster. The method includes initiating retrieval of a machine-learning model from a data repository and converting the machine-learning model to an executable format. The method includes transmitting the converted machine-learning model to each node of the server cluster and executing the converted machine-learning model on each node. The method includes generating an initial performance metric based on execution of the converted machine-learning model on each node. The method includes transmitting the plurality of initial performance metrics from each node to an external processor and combining the plurality of initial performance metrics to produce a combined performance metric. The method includes modifying a model hyperparameter of the machine-learning model based on the combined performance metric and executing the modified machine-learning model in a computer system to evaluate real-time event data.
Systems, methods, and computer program products are provided for processing a payment transaction over an electronic network. The method includes receiving an authorization request message, including first transaction data associated with an electronic payment transaction and generating a unique identifier associated with the electronic payment transaction. The method further includes receiving an authorization response message, including second transaction data associated with the electronic payment transaction, indicating that the electronic payment transaction is authorized. The method further includes storing a portion of the first transaction data and the second transaction data in association with the unique identifier for the electronic payment transaction. The method further includes transmitting a response message comprising the unique identifier and the authorization decision and receiving a clearing request message comprising the unique identifier. The method further includes retrieving the stored transaction data and initiating clearing of the electronic payment transaction.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
37.
SECURE DATA EXCHANGE MATCHING ACROSS IDENTITY PROVIDERS
A method includes receiving a first encrypted first identity attribute. A first doubly encrypted first identity attribute is formed by encrypting the first encrypted first identity attribute. A second doubly encrypted first identity attribute is formed by encrypting the first encrypted first identity attribute. They are transmitted to a user device, which removes a user layer of encryption on each to form a second encrypted first identity attribute and a third encrypted first identity attribute. Layers of encryption are added to the second encrypted first identity attribute to form a third doubly encrypted first identity attribute and the third encrypted first identity attribute to form a fourth doubly encrypted first identity attribute. The server computer receives them and transmits, to the second identity provider computer, the fourth doubly encrypted first identity attribute. The second identity provider computer obtains a first identity attribute and compares it to a second identity attribute.
H04L 9/14 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité utilisant plusieurs clés ou algorithmes
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
38.
METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR SIMPLIFYING TRANSFORMER FOR SEQUENTIAL RECOMMENDATION
Methods, systems, and computer program products may simplify Transformer machine learning models for sequential recommendation via a softmax-free gated attention mechanism and/or may use a gated unit to further sparsify attentions, which may simplify attention distributions and reduce negative impacts of noisy items.
Provided is a method for automatically selecting one of a plurality of payment cards for contactless payment. The method may include receiving, by a first priority input circuit of a first payment card of a plurality of payment cards, a priority adjusting input from a user. In response to receiving the priority adjusting input, adjusting the respective priority indicator of the first payment card based on the priority adjusting input. The method may further include, receiving by a respective communication circuit of each respective payment card of the plurality of payment cards, a polling request from a transaction terminal. In response to receiving the polling request, transmitting, by each respective communication circuit of each respective payment card of the plurality of payment cards, a respective response to the polling request. The response to the polling request may include a respective priority indicator for the respective payment card.
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p.ex. cartes à puces ou cartes magnétiques
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
A method is disclosed. The method includes receiving, by a user device from the access device, a routing path list comprising a first set of network nodes. After receiving the routing path list, the user device determines a routing options list comprising a second set of network nodes based on the first set of network nodes in the routing path list. The method also includes obtaining an encrypted credential or token, and transmitting, by the user device to the access device, the routing options list, and the encrypted credential or token to the access device. The access device transmits an authorization request message comprising the encrypted credential or token, and the routing options list to a server computer via at least some of the network nodes in the second set of network nodes. The server computer may be an authorizing entity computer.
H04L 45/02 - Mise à jour ou découverte de topologie
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
Methods, systems, and computer program products are provided for encoding feature interactions based on tabular data. An exemplary method includes receiving a dataset in a tabular format including a plurality of rows and a plurality of columns. Each column is indexed to generate a position embedding matrix. Each column is grouped based on at least one tree model to generate a domain embedding matrix. An input vector is generated based on the dataset, the position embedding matrix, and the domain embedding matrix. The input vector is inputted into a first multilayer perceptron (MLP) model to generate a first output vector, which is transposed to generate a transposed vector. The transposed vector is inputted into a second MLP model to generate a second output vector, which is inputted into at least one classifier model to generate at least one prediction.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06F 16/901 - Indexation; Structures de données à cet effet; Structures de stockage
Enrollment data packet including device information of a user and resource provider information is received by a processing computer from an authorizing entity computer. The processing computer generates a token request push data packet including user information and the device information and transmits, to user device, the token request push data packet. The user device then transmits a request to initiate token provisioning to a resource provider computer associated with the resource provider information. Provisioning request generated based on the request to initiate the token provisioning is received by the processing computer from the resource provider computer and transmitted to a token service computer. The provisioning request includes the token request push data packet. Upon receiving the provisioning request, the token service computer determines token data using the device information and the user information and provides the token data to the resource provider computer.
Provided are systems for generating a machine learning model for classification tasks using unadversarial training that include a processor to perform an unadversarial training procedure to train a machine learning model to provide a trained machine learning model. When performing the unadversarial training procedure, the processor is programmed or configured to receive a training dataset including a plurality of training samples; generate a noise vector for the plurality of training samples based on a uniform distribution; perturb each training sample of the plurality of training samples; obtain a gradient; generate an updated noise vector based on the gradient; perturb each training sample of the plurality of training samples based on the updated noise vector; and update a model weight of the machine learning model based on the second plurality of perturbed training samples to provide the trained machine learning model. Methods and computer program products are also provided.
Systems, methods, and computer program products are provided for saving memory during training of knowledge graph neural networks. The method includes receiving a training dataset including a first set of knowledge graph embeddings associated with a plurality of entities for a first layer of a knowledge graph, inputting the training dataset into a knowledge graph neural network to generate at least one further set of knowledge graph embeddings associated with the plurality of entities for at least one further layer of the knowledge graph, quantizing the at least one further set of knowledge graph embeddings to provide at least one set of quantized knowledge graph embeddings, storing the at least one set of quantized knowledge graph embeddings in a memory, and dequantizing the at least one set of quantized knowledge graph embeddings to provide at least one set of dequantized knowledge graph embeddings.
Systems, methods, and computer program products may (i) obtain a graph including a plurality of edges and a plurality of nodes for the plurality of edges, each labeled node of a subset of labeled nodes being associated with a label, and each unlabeled node of a subset of unlabeled nodes not being associated with a label; (ii) train, using the graph and the label for each labeled node, a graph neural network (GNN), wherein training the GNN generates a prediction for each node; (iii) generate, from the subset of unlabeled nodes, a candidate pool of candidate nodes; (iv) generate, using a label propagation algorithm, a predicted label for each candidate node; (v) select a candidate node of the candidate pool of candidate nodes that is associated with a greatest hybrid entropy reduction for the graph; and (vi) provide the selected candidate node for labeling.
Embodiments can perform a database join that allows secret shared database join between a database table with a unique matching column and a database table with a non-unique matching column (i.e., contains unbounded repeats of values).
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p.ex. par clés ou règles de contrôle de l’accès
G06F 16/176 - Support d’accès partagé aux fichiers; Support de partage de fichiers
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuées; Architectures de systèmes de bases de données distribuées à cet effet
47.
PRIVACY-PRESERVING DETECTION FOR DIRECTIONAL ELECTRONIC COMMUNICATIONS
Embodiments are directed to methods and systems that can be used to perform efficient, parallel, privacy-preserving graph analysis. One particular application of embodiments is performing private cycle detection in order to detect anomalous behavior in directional electronic communications. Two (or more) parties can each possess private electronic communication data, which can be used to construct a private directed union graph corresponding to the union of the parties' electronic communication data. This private union graph can be analyzed by a multi-party computation network in order to detect cycles of defined length (e.g., comprising between four and eight communicating participants). These cycles can be used as evidence of anomalous or illicit use of such electronic communications systems.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
Systems, methods, and computer program products are provided for flexible transaction message routing. An exemplary method includes receiving an authorization request message associated with a payment transaction. The authorization request message includes a first account identifier associated with a user. A second account identifier is determined from a plurality of account identifiers based on at least one rule associated with the first account identifier. A modified authorization request message for the payment transaction is generated. The modified authorization request message includes the second account identifier. The modified authorization request message is transmitted to an issuer system associated with the second account identifier. A transaction history record based on the payment transaction and the second account identifier is stored.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/42 - Confirmation, p.ex. contrôle ou autorisation de paiement par le débiteur légal
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
A method is disclosed. It includes establishing a communication session between a vehicle comprising a first processor and an energy supply terminal comprising a second processor. The method includes obtaining, by the first processor in the vehicle, a credential or token, and then generating an encrypted data packet including the credential or token. The method also includes providing the encrypted data packet to the energy supply terminal. The energy supply terminal transmits the encrypted data packet to a service provider computer, which decrypts the encrypted data packet to obtain the credential or token, and then processes a transaction for supplying the vehicle with energy using the credential or the token.
B60L 53/30 - PROPULSION DES VÉHICULES À TRACTION ÉLECTRIQUE; FOURNITURE DE L'ÉNERGIE ÉLECTRIQUE À L'ÉQUIPEMENT AUXILIAIRE DES VÉHICULES À TRACTION ÉLECTRIQUE; SYSTÈMES DE FREINS ÉLECTRODYNAMIQUES POUR VÉHICULES, EN GÉNÉRAL; SUSPENSION OU LÉVITATION MAGNÉTIQUES POUR VÉHICULES; CONTRÔLE DES PARAMÈTRES DE FONCTIONNEMENT DES VÉHICULES À TRACTION ÉLECTRIQUE; DISPOSITIFS ÉLECTRIQUES DE SÉCURITÉ POUR VÉHICULES À TRACTION ÉLECTRIQUE Échange d'éléments d’emmagasinage d'énergie dans les véhicules électriques - Détails de construction des stations de charge
B60L 53/31 - Colonnes de charge spécialement adaptées aux véhicules électriques
B60L 53/66 - Transfert de données entre les stations de charge et le véhicule
B60L 53/18 - Câbles spécialement adaptés pour recharger des véhicules électriques
Embodiments allow a user of a first user device transfer funds to a recipient having a second user device. The first user device includes a communication device including a transfer application linked to a first account of the user. The second user device includes a payment card or a communication device that does not have the transfer application. Tapping the first user device to the second user device transmits an account identifier of a recipient account from the second user device to the first user device. The transfer application receives the recipient account identifier and an amount to be transferred to the recipient account. The transfer application generates a push request and transmits the push request to the processing network, which coordinates debiting the amount to the first account and crediting the amount to the recipient account.
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p.ex. cartes à puces ou cartes magnétiques
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
Private set intersection (PSI) protocols can be efficiently performed for sets of disparate sizes. A server can compute an array, such as an inverted Bloom filter or cuckoo hash table, that represents the content of the server set. A client can query the array, e.g., using a private information retrieval (PIR) protocol, to obtain information that enables the client to determine whether a particular element of the client's set is also in the server's set. By repeating the query for each element of the client's set, the client can learn the intersection.
H04L 9/06 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p.ex. système DES
52.
SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR EFFICIENTLY JOINING TIME-SERIES DATA TABLES
Described are a system, method, and computer program product for efficiently joining time-series data tables. The method includes loading a first table and a second table into a memory and generating a set of first key-value pairs based on a set of first time-series records and a set of second key-value pairs based on a set of second time-series records. The method also includes sorting the set of first key-value pairs and the set of second key-value pairs. The method further includes interleaving the set of first key-value pairs with the set of second key-value pairs and sequentially matching the sets of time-series records to form a joined table. The method further includes, in response to matching each respective second time-series record with the respective first time-series record, removing the respective second time-series record from the at least one memory.
Provided are systems, methods, and computer program products for dynamic peer group analysis for systematic changes in large scale data. Data associated with a plurality of entities is received and a relational graph is generated based on the data. A target entity is selected and a peer group for the target entity is determined based on the relational graph. An average and a standard deviation of the risk scores of the peer group are calculated and used to determine whether a systematic change in the behavior of the peer group has occurred. Whether a change in behavior of the target entity is a false anomaly or a true anomaly is determined based on whether a systematic change in the behavior of the peer group has occurred. An action is performed based on whether the change in behavior of the target entity is a false anomaly or a true anomaly.
A data owner can provide shares of a cryptographic key to N key servers. The N key servers can store the shares of a cryptographic key from the data owner such that T shares of the cryptographic key can be used to reconstruct the cryptographic key. A client computer can send a blinded query to T key servers of the N key severs, wherein the T key servers can encrypt a blinded query of a client computer using the share of the cryptographic key to determine a partial encryption. The client computer can receive T partial encryptions, assemble T partial encryptions to form an encrypted blinded query, and deblind the encrypted blinded query. The client computer can then use the encrypted query to perform a search on encrypted data of a remote database server using a searchable symmetric encryption scheme.
H04L 9/14 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité utilisant plusieurs clés ou algorithmes
H04L 9/30 - Clé publique, c. à d. l'algorithme de chiffrement étant impossible à inverser par ordinateur et les clés de chiffrement des utilisateurs n'exigeant pas le secret
A method includes receiving, by a user device, an interaction request message for an interaction. The interaction request message comprises a requested amount from a resource provider computer. A secure element on the user device selects between an offline balance and an offline amount of program tokens stored in the secure element. The offline amount of program tokens can be selected. The secure element on the user device can deduct the requested amount from the offline amount of program tokens. The user device can complete the interaction with the resource provider computer.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/06 - Circuits privés de paiement, p.ex. impliquant de la monnaie électronique utilisée uniquement entre les participants à un programme commun de paiement
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de banque à domicile
56.
SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR INTERPRETING BLACK BOX MODELS BY PERTURBING TRANSACTION PARAMETERS
A computer-implemented method includes: receiving an inquiry request message identifying a first payment transaction having a plurality of transaction parameters and a risk score, where the risk score is generated by a machine-learning model based on the plurality of transaction parameters; for each transaction parameter of the plurality of transaction parameters, perturbing a value of the transaction parameter and re-analyzing the first payment transaction with the machine-learning model to generate a perturbed risk score based on the perturbed transaction parameter; determining at least one impact parameter from the plurality of transaction parameters by comparing the perturbed risk scores generated for each of the plurality of transaction parameters; and generating an inquiry response message based on the at least one impact parameter.
A method is disclosed. The method includes receiving, by a token service computer, a request to obtain a token, and then obtaining the token. The method also includes receiving a request to activate the token, after the token is used to conduct a transaction.
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
A method includes a sender device operated by a sender receiving a receiver address associated with a receiver. The sender device prompts the sender to interact a card comprising a processor and a memory storing a sender public key and a sender private key of a sender public-private key pair associated with a blockchain network, the card held by the sender. The sender device transmits interaction data including the receiver address, a sender address of the sender, and a value to the card. The processor of the card retrieves the sender private key and signs the interaction data to produce signed interaction data. The sender device receives the signed interaction data and the sender public key. The sender device transmits the interaction data and the signed interaction data to the blockchain network. The blockchain network records the interaction data and the signed interaction data in a block of a blockchain.
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04L 67/1097 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour le stockage distribué de données dans des réseaux, p.ex. dispositions de transport pour le système de fichiers réseau [NFS], réseaux de stockage [SAN] ou stockage en réseau [NAS]
A method includes transmitting an authorization request message with a credential or a token associated with a first user to an authorizing entity computer, and then receiving, from the authorizing entity computer, an authorization response message; and responsive to receiving the authorization response message. The method also includes transmitting the credential or the token to a vehicle. The first user is able to access the vehicle by presenting a user device that contains the credential or token to the vehicle.
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
60.
METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR MANAGING CONFIGURATION LEASE
Provided is a computer-implemented method, system, and computer program product for leasing decoupled configurations and managing configuration lease persistence with application state management including receiving a configuration set lease request from a client application in response to the client application being launched. In response to receiving the configuration set lease request, the method, system, and computer program product includes determining a unique configuration set from a pool of different configuration sets. Further, the method, system, and computer program product includes communicating the unique configuration set to the client application and activating a lease of the unique configuration set by associating the client application with the unique configuration set in a lease database. In response to determining that the lease is valid, persisting the lease in the lease database. In response to determining that the lease is invalid, deactivating the lease of the unique configuration set in the lease database.
A method includes a server computer receiving, from a first data provider computer, encrypted data derived from first identity data and a cryptographic key or derivative thereof stored at the first data provider computer. The server computer transmits, to a second data provider computer, the encrypted data and/or the cryptographic key or derivative thereof. The server computer receives, from the second data provider computer, intermediate data derived from second identity data stored at the second data provider computer. The server computer determines if the first identity data and the second identity data are duplicates while the first identity data and the second identity data are encrypted. The server computer removes one of encrypted first identity data, derived from the first identity data, and encrypted second identity data, derived from the second identity data, from a memory in the server computer.
The present disclosure discloses a method and a system for performing transaction. In an embodiment, when a user initiates a card transaction at an entity, the method comprises receiving card information of the user from the entity for performing transaction. In response to receiving the card information, the method comprises identifying whether an alternate identifier is present for the card information in a. first server. If the alternate identifier is present in the first server, the method comprises transmitting the alternate identifier from the first server and a cryptogram value associated with the alternate identifier to the entity for performing the transaction. If the alternate identifier is not present in the first server, the method comprises transmitting the alternate identifier for the card information by obtaining the alternate identifier from a second server and the cryptogram value associated with the alternate identifier to the entity for performing the transaction.
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
G06Q 20/02 - Architectures, schémas ou protocoles de paiement impliquant un tiers neutre, p.ex. une autorité de certification, un notaire ou un tiers de confiance
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p.ex. cartes à puces ou cartes magnétiques
G06Q 20/14 - Architectures de paiement spécialement adaptées aux systèmes de facturation
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
63.
METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR GENERATING ROBUST GRAPH NEURAL NETWORKS USING UNIVERSAL ADVERSARIAL TRAINING
Described are a method, system, and computer program product for generating robust graph neural networks using universal adversarial training. The method includes receiving a graph neural network (GNN) model and a bipartite graph including an adjacency matrix, initializing model parameters of the GNN model, initializing perturbation parameters, and sampling a subgraph of a complementary graph based on the bipartite graph. The method further includes repeating until convergence of the model parameters: drawing a random variable from a uniform distribution; generating a universal perturbation matrix based on the subgraph, the random variable, and the perturbation parameters; determining Bayesian Personalized Ranking (BPR) loss by inputting the bipartite graph and the universal perturbation matrix to the GNN model; updating the perturbation parameters based on stochastic gradient ascent; and updating the model parameters based on stochastic gradient descent. The method further includes, in response to convergence of the model parameters, outputting the model parameters.
A method, system, and computer program product is provided for secure data distribution. The system includes at least one processor programmed or configured to receive, from a first system, a data capture request, generate a data capture object including a plurality of orchestration rules and a first public key, digitally sign the data capture object with a second private key corresponding to a second public key, transmit the data capture object to the first system, receive encrypted user data including user data encrypted with the first public key, generate a transient token based on the user data and the plurality of orchestration rules, and distribute the transient token to each party of the plurality of parties by transmitting the transient token to the first system via a device.
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
65.
PRIVACY-PRESERVING BIOMETRICS FOR MULTI-FACTOR AUTHENTICATION
A method includes generating a second public key and a second private key of a second public-private key pair, and transmitting the second public key to a first user device, which stores an encrypted biometric template. The encrypted biometric template is a biometric template encrypted with a first public key of a first public-private key pair. The first user device encrypts the encrypted biometric template with the second public key to form a double encrypted biometric template. The method includes receiving the double encrypted biometric template from the first user device, decrypting the double encrypted biometric template using the second private key to obtain the encrypted biometric template, determining a test biometric template and encrypting the test biometric template, comparing the encrypted test biometric template and the encrypted biometric template to obtain an encrypted biometric match score, and transmitting the encrypted biometric match score to a server computer.
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
A method is disclosed and includes executing an integrated application comprising an SDK (software development kit) on a user device with a processor. The method includes determining, by the SDK and the processor on the user device, an checksum for the integrated application, validating, by the SDK in the user device, the integrated application using the determined checksum, and responsive to validating the determined checksum, performing, by the integrated application on the user device, an action.
Described are a system, method, and computer program product for secure edge computing of a machine learning model. The method includes transmitting, with a server, a first portion of a machine learning model to a computing device remote from the server. The first portion includes at least one first layer of the machine learning model configured to process a first input of data collected by the computing device and generate an output. The method also includes receiving, with the server from the computing device, encoded model data including the output. The method further includes decoding, with the server, the encoded model data to produce decoded model data, and generating, with the server, a classification based on the first input of data by executing a second portion of the machine learning model.
G06F 18/241 - Techniques de classification relatives au modèle de classification, p.ex. approches paramétriques ou non paramétriques
G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
G06F 16/90 - Recherche d’informations; Structures de bases de données à cet effet; Structures de systèmes de fichiers à cet effet - Détails des fonctions des bases de données indépendantes des types de données cherchés
A computer-implemented method includes: receiving an inquiry request message identifying a first payment transaction having a first plurality of transaction parameters and a first authorization decision; querying a database including transaction data associated with a plurality of historical payment transactions to identify a subset of historical payment transactions, the transaction data including, for each of the plurality of historical payment transactions, a plurality of transaction parameters and an authorization decision, the subset of historical payment transactions including payment transactions having an authorization decision different from the first authorization decision and having a similarity score that satisfies a threshold; determining an impact parameter of the first plurality of transaction parameters by comparing the first plurality of transaction parameters with the plurality of transaction parameters associated with the plurality of historical payment transactions in the subset; and generating an inquiry response message based on the impact parameter.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 10/0635 - Analyse des risques liés aux activités d’entreprises ou d’organisations
Methods, systems, and computer program products are provided for energy efficient generation of artificial noise to prevent side-channel attacks. An example method includes storing at least one secret value including secret value bits. At least one cryptographic operation is executed based on the at least one secret value. An artificial sequence generator stores at least one state indication based on a plurality of previous cryptographic operations executed on the device. A plurality of samples of artificial noise are generated, and a number of the plurality of samples is based on at least one power constraint parameter. Each sample of artificial noise of the plurality of samples of artificial noise is overlaid over a respective portion of a side channel signal based on the at least one state indication to mask leakage information associated with the at least one secret value on the side channel signal.
G06F 21/55 - Détection d’intrusion locale ou mise en œuvre de contre-mesures
G06F 7/72 - Méthodes ou dispositions pour effectuer des calculs en utilisant une représentation numérique non codée, c. à d. une représentation de nombres sans base; Dispositifs de calcul utilisant une combinaison de représentations de nombres codées et non codées utilisant l'arithmétique des résidus
G06F 9/44 - Dispositions pour exécuter des programmes spécifiques
G06F 21/00 - Dispositions de sécurité pour protéger les calculateurs, leurs composants, les programmes ou les données contre une activité non autorisée
H04L 9/00 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité
70.
SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR DYNAMIC NODE CLASSIFICATION IN TEMPORAL-BASED MACHINE LEARNING CLASSIFICATION MODELS
Described are a system, method, and computer program product for dynamic node classification in temporal-based machine learning classification models. The method includes receiving graph data of a discrete time dynamic graph including graph snapshots, and node classifications associated with all nodes in the discrete time dynamic graph. The method includes converting the discrete time dynamic graph to a time-augmented spatio-temporal graph and generating an adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph. The method includes generating an adaptive information transition matrix based on the adjacency matrix and determining feature vectors based on the nodes and the node attribute matrix of each graph snapshot. The method includes generating and propagating initial node representations across information propagation layers using the adaptive information transition matrix and classifying a node of the discrete time dynamic graph subsequent to the first time period based on final node representations.
G06F 18/2323 - Techniques non hiérarchiques basées sur la théorie des graphes, p.ex. les arbres couvrants de poids minimal [MST] ou les coupes de graphes
G06F 16/90 - Recherche d’informations; Structures de bases de données à cet effet; Structures de systèmes de fichiers à cet effet - Détails des fonctions des bases de données indépendantes des types de données cherchés
G06F 18/241 - Techniques de classification relatives au modèle de classification, p.ex. approches paramétriques ou non paramétriques
A method performed by an access server is disclosed. The method including receiving a first access request including various fields of data for accessing a resource. The method may then generate a first fingerprint using a first value of a first field of the first access request and store the first fingerprint. After, the access server may receive a second access request, and generate a second fingerprint using a second value of the first field of the second access request. Then the first fingerprint can be compared to the second fingerprint to determine a possible match of the second access request to the first access request. A database is accessed using data of the first or second access request, to retrieve missing data in the first or second access request. The missing data can be compared to a corresponding field of the other access request to confirm a match.
Provided are systems for ensemble learning with machine learning models that include a processor to receive a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, add an amount of time delay to one or more data instances to provide an augmented training dataset, select a first plurality of supervised machine learning models, select a second plurality of unsupervised machine learning models, train the first plurality of supervised machine learning models and the second plurality of unsupervised machine learning models based on the augmented training dataset, generate an ensemble machine learning model based on outputs of the supervised machine learning models and unsupervised machine learning models, and generate a runtime output of the ensemble machine learning model based on a runtime input to the ensemble machine learning model. Methods and computer program products are also provided.
Methods for authenticating digital transactions include receiving a device registration request, a device attestation response including a first token, and a selection of an authentication mode from a device. In response to receiving the device registration request and determining that the selected authentication mode is a static personal identification number (PIN) authentication mode, a device registration response is provided to the device. A first payment transaction request and an enrolment request to authenticate a second payment transaction request using the static PIN authentication mode are subsequently received from the device. The device is communicated with to receive the static PIN from the device. The device is enrolled based on the static PIN. Systems and computer program products are also provided.
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
74.
SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR SYSTEM MACHINE LEARNING IN DEVICE PLACEMENT
Systems, methods, and computer program products that use unsupervised learning to learn relationships between operations of a machine learning model based on a model graph representation to group the operations into clusters and, given a set of clusters and labels for the clusters, use a reinforcement learning algorithm to generate a final device placement result for the machine learning model.
Provided are systems for controlling a data pipeline in a data pipeline ecosystem that include at least one processor to receive metadata parameters for a data pipeline, store the metadata parameters in a data repository, generate a logical representation of the data pipeline based on the metadata parameters, execute the data pipeline based on the metadata parameters of the data pipeline, and model the data pipeline using the directed acyclic graph (DAG) of the data pipeline. Methods and computer program products are also provided
Methods and systems for performing efficient integration tests on mobile device for contactless data transfers are described. Rather than performing contactless communications with a variety of test user devices (e.g., test smart cards), which may be time consuming and may present physical difficulty, a mobile device can simulate the result of these communications using a simulator application operating on the mobile device. A contactless communication application, also operating on the mobile device, can communicate with the simulator application in order to generate interaction payloads based on stored data records corresponding to the test user devices. These interaction payloads can then be transmitted by the mobile device to a processing computer. Later, the mobile device may receive a response from the processing computer or another computer system, indicating if the interaction payloads were successfully received and interpreted. This in turn may indicate if the integration test was successful.
Methods, systems, and computer program products are provided for cleaning noisy data from unlabeled datasets using autoencoders. A method includes receiving training data including noisy samples and other samples. An autoencoder network is trained based on the training data to increase a first metric based on the noisy samples and to reduce a second metric based on the other samples. Unlabeled data including unlabeled samples is received. A plurality of third outputs is generated by the autoencoder network based on the plurality of unlabeled samples. For each respective unlabeled sample, a respective third metric is determined based on the respective unlabeled sample and a respective third output, and whether to label the respective unlabeled sample as noisy or clean is determined based on the respective third metric and a threshold. Each respective unlabeled sample determined to be labeled as noisy is cleaned.
A hub computer receives, from a first computer, a sender message comprising a promise corresponding to a transaction comprising a promise type, an amount, a first verification key associated with the first computer, computer code, and a digital signature. The hub computer verifies the promise by at least verifying the digital signature using the first verification key, verifying that the amount is less than a first computer amount, and verifying that the hub computer is able to process the promise type. The hub computer executes the computer code to perform the transaction.
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
G06Q 20/02 - Architectures, schémas ou protocoles de paiement impliquant un tiers neutre, p.ex. une autorité de certification, un notaire ou un tiers de confiance
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
A method, system, and computer program product is provided for graph-based fraud detection. The system includes at least one processor programmed or configured to generate a graph data structure based on a plurality of transactions between a plurality of accounts, wherein each account of the plurality of accounts is represented by a node in the graph data structure, and wherein each transaction of the plurality of transactions is represented by an edge in the graph data structure, determine a plurality of features of the graph data structure for each account of the plurality of accounts, generate a graph profile for at least one account of the plurality of accounts based on the plurality of features for the at least one account, and update the graph profile for the at least one account based on at least one new transaction engaged in by the at least one account.
G06F 18/2323 - Techniques non hiérarchiques basées sur la théorie des graphes, p.ex. les arbres couvrants de poids minimal [MST] ou les coupes de graphes
80.
METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR COMMUNITY DETECTION
Methods, systems, and computer program products for community detection: (i) obtain a plurality of node embeddings associated with a graph; (ii) determine a number of clusters into which the plurality of node embeddings is to be clustered; (iii) cluster, based on distances between pairs of node embeddings, the plurality of node embeddings into the number of clusters until, for each node embedding in each cluster, a node associated with that node embedding is within k-hops in the graph of each other node associated with each other node embedding in that cluster; (iv) reposition centroids of the number of clusters; (v) repeat steps (iii) and (iv) until a first stopping criteria is satisfied; (vi) repeat steps (ii) through (v) until a second stopping criteria that depends on a conductance of a clustering including the number of clusters is satisfied; and (vii) provide the clustering including the number of clusters.
Embodiments of the present disclosure are directed to onboarding a model from a training platform to an inference platform and selecting parameters of the model to optimize performance of the model. For example, the onboarding of the model to the inference platform can be based on a series of interactions between a model onboarding systems at the training platform and at the inference platform. An optimization process can include a searching-based process to derive optimal settings for the model. The optimization process can simulate feature combinations of the model and identify an optimal combination of settings of the model for increased model performance.
Embodiments of the present disclosure enable users to efficiently verify digital data produced by queried databases, even when that data is differentially-private (e.g., satisfying the conditions of differential privacy in order to protect sensitive or private data). In addition to the query result, a database computer can provide the client with a non-interactive zero-knowledge proof (NIZK), data that the client can use to verify the digital data contained in the query result, without revealing any private data to the client. Various innovations, including vectorized proofs, enable the database computer to generate proofs that require less data (e.g., when measured in bytes) than most NIZK proof systems. Consequently, these proofs can be transmitted and verified more quickly and efficiently. Embodiments of the present disclosure can make use of partially or homomorphic commitments and efficient vector proof techniques to achieve these performance improvements.
Described are a system, method, and computer program product for real-time transactions. The method includes receiving a real-time payment identifier request, the real-time payment identifier request including at least one of a phone number associated with the user device and an account identifier. The real-time payment identifier request may be communicated to a real-time payment platform located remotely from the user device. A real-time payment identifier may be received and stored in a real-time payment identifier database stored on the user device. A first transaction identifier request may be received from a first merchant system. The real-time payment identifier may be communicated to the first merchant system. A second transaction identifier request may be received from a second merchant system and the real-time payment identifier may be communicated to the second merchant system.
Methods, systems, and computer program products for auto-profiling anomalies: receive anomaly transactions, select a subset of anomaly transactions, the subset of anomaly transactions being associated with a plurality of features, generate, based on the plurality of features and a distribution of the plurality of features, weights associated with the plurality of features; segment, using an unsupervised clustering algorithm, based on the plurality of features and the plurality of weights, the subset of anomaly transactions into a plurality of segments of anomaly transactions; and label a subset of segments of the plurality of segments with a feature profile including a feature from each segment of the subset of segments associated with a highest weight of the plurality of weights of the plurality of features of the anomaly transactions in that segment.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06F 16/28 - Bases de données caractérisées par leurs modèles, p.ex. des modèles relationnels ou objet
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
85.
SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR DENOISING SEQUENTIAL MACHINE LEARNING MODELS
Described are a system, method, and computer program product for denoising sequential machine learning models. The method includes receiving data associated with a plurality of sequences and training a sequential machine learning model based on the data associated with the plurality of sequences to produce a trained sequential machine learning model. Training the sequential machine learning model includes denoising a plurality of sequential dependencies between items in the plurality of sequences using at least one trainable binary mask. The method also includes generating an output of the trained sequential machine learning model based on the denoised sequential dependencies. The method further includes generating a prediction of an item associated with a sequence of items based on the output of the trained sequential machine learning model.
A method, system, and computer program product is provided for embedding compression and reconstruction. The method includes receiving embedding vector data comprising a plurality of embedding vectors. A beta-variational autoencoder is trained based on the embedding vector data and a loss equation. The method includes determining a respective entropy of a respective mean and a respective variance of each respective dimension of a plurality of dimensions. A first subset of the plurality of dimensions is determined based on the respective entropy of the respective mean and the respective variance for each respective dimension of the plurality of dimensions. A second subset of the plurality of dimensions is discarded based on the respective entropy of the respective mean and the respective variance for each respective dimension of the plurality of dimensions. The method includes generating a compressed representation of the embedding vector data based on the first subset of dimensions.
Systems, methods, and computer program products for multi-domain ensemble learning based on multivariate time sequence data are provided. A method may include receiving multivariate sequence data. At least a portion of the multivariate sequence data may be inputted into a plurality of anomaly detection models to generate a plurality of scores. The multivariate sequence data may be combined with the plurality of scores to generate combined intermediate data. The combined intermediate data may be inputted into a combined ensemble model to generate an output score. In response to determining that the output score satisfies a threshold, at least one of an alert may be communicated to a user device, the multivariate sequence data may be inputted into the feature-domain ensemble model to generate a feature importance vector, or at least one of a model-domain, a time-domain, a feature-domain, or the combined ensemble model may be updated.
Systems, methods, and computer program products for determining long-range dependencies using a non-local graph neural network (GNN): receive a dataset comprising historical data; generate at least one layer of a graph neural network by generating graph convolutions to compute node embeddings for a plurality of nodes of the dataset, the graph convolutions generated by aggregating node data from a first node of the dataset and node data from at least one second node comprising a neighbor node of the first node; cluster the node embeddings to form a plurality of centroids; determine an attention operator for at least one node-centroid pairing, the at least one node-centroid pairing comprising the first node and a first centroid; and generate relational data corresponding to a relation between the first node and at least one third node comprising a non-neighbor node of the first node using the attention operator.
A method is disclosed. The method includes receiving and storing, by a processing computer, a set of user data associated with a user, and an encrypted data packet from an authorizing entity computer, the encrypted data packet comprising sensitive data associated with the user encrypted using a first cryptographic key. The method includes receiving, from a user device, a request comprising at least some user data in the set of user data, determining the encrypted data packet corresponding to the at least some of the user data, and responsive to determining the encrypted data packet, obtaining a second cryptographic key. The method also includes decrypting the encrypted data packet with the second cryptographic key to obtain the sensitive data, and processing a transaction using the sensitive data or a derivative thereof.
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
90.
SECURE DEVICE INFORMATION DISPLAY WITH AUTHENTICATION USING SOFTWARE DEVELOPMENT KIT (SDK)
A method is disclosed. The method can be performed by at least a mobile device comprising a processor, and memory and display coupled to the processor, the memory storing an application comprising an SDK. The method comprises transmitting, by the SDK, an access credential identifier associated with a main credential to a processing computer. The processing computer then initiates an authentication process with respect to a user of the main credential. The processing computer then receives the main credential and additional data associated with the main credential. The method then includes the SDK receives the main credential and additional data from the processing computer. After the SDK receives the data, the main credential and the additional data are displayed on a display of the mobile device via the SDK.
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
A method is disclosed. The method includes determining, by a delegated certificate authority computer, a tier from a plurality of tiers for a digital wallet provider based on a list of qualifying criteria. The method also includes generating a digital certificate based on the tier, where the digital certificate is used by a digital wallet application computer associated with the digital wallet provider to complete interactions using a digital currency maintained by a blockchain network. The method further includes transmitting, by the delegated certificate authority computer to a digital wallet application computer, the digital certificate.
G06F 21/33 - Authentification de l’utilisateur par certificats
G06F 21/64 - Protection de l’intégrité des données, p.ex. par sommes de contrôle, certificats ou signatures
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
H04L 9/00 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité
92.
METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR UNSUPERVISED ALIGNMENT OF EMBEDDING SPACES
Provided are methods, systems, and computer program products for unsupervised alignment of embedding spaces. A method may include receiving a first embedding matrix and a second embedding matrix. The first embedding matrix may include a plurality of source points and the second embedding matrix may include a plurality of target points. An initial permutation matrix and an initial orthogonal matrix may be initialized. A permutation matrix may be determined based on the initial permutation matrix, the first embedding matrix, and the second embedding matrix. An orthogonal matrix may be determined based on the initial orthogonal matrix, the first embedding matrix, the permutation matrix, and the second embedding matrix. For each step of a target number of steps, the following may be repeated: updating the permutation matrix based on a quantized 2-Wasserstein distance, and updating the orthogonal matrix based on a gradient descent and a Procrustes problem.
G06F 7/76 - Dispositions pour le réagencement, la permutation ou la sélection de données selon des règles prédéterminées, indépendamment du contenu des données
G06F 17/18 - Opérations mathématiques complexes pour l'évaluation de données statistiques
G06F 40/00 - Maniement de données en langage naturel
G10L 25/30 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par la technique d’analyse utilisant des réseaux neuronaux
93.
STATISTICALLY RECEIVER PRIVATE OBLIVIOUS TRANSFER FROM CDH
Novel methods of performing statistically receiver private (SRP) string oblivious transfer (OT) are disclosed. Such methods can be used to transfer messages between senders and receivers subject to the conditions of oblivious transfer. These methods can be used as a "building block" to develop useful cryptographic systems, such as multiparty computation networks. A sender computer and a receiver computer can exchange a first and second oblivious transfer message. Data contained in these messages can be used, by the sender computer, to obfuscate a first message and a second message. The sender computer can transmit (in a third oblivious transfer message), both the first obfuscated message, the second obfuscated message and a group element to a receiver computer. Using the group element, the receiver computer can attempt to de-obfuscate one or both of the obfuscated messages, and can receive either a first message or a second message in the process.
Methods for performing oblivious transfer are disclosed. These methods include a method for performing random single bit oblivious transfer (a "first method"), a method for performing random string oblivious transfer (a "second method"), and a method for performing non-random string oblivious transfer (a "third method"). In the first method, a sender computer can use a hardcore predicate function to obfuscate either a first message or a second message, generating an obfuscated message. The receiver computer can de-obfuscate this obfuscated message to randomly receive either the first message or the second message. The second method and third method can be implemented, with some modification, by repeatedly performing the first method, once for each "message bit" of the sender's messages. In the second and third methods, the receiver computer can send "indicator bits" to the sender computer, enabling the sender computer to transmit a random or non-random message strings to the receiver..
A disclosed method includes receiving, by a first device from a server computer, a first hash value along with a plurality of other hash values, and a random value. The first hash value is generated by inputting at least a first credential and the random number into a hash function. The method includes reading a second credential from a second device operated by a second user, and generating a second hash value by inputting at least the second credential and the random value into the hash function. The method includes comparing the first hash value and the second hash value, and determining that the first hash value and the second hash value match. The method also includes validating an action of the second user when the first hash value and the second hash value match.
G06Q 20/42 - Confirmation, p.ex. contrôle ou autorisation de paiement par le débiteur légal
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06K 7/10 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04L 9/06 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p.ex. système DES
96.
REPLICATED SECRET SHARE GENERATION FOR DISTRIBUTED SYMMETRIC CRYPTOGRAPHY
Methods and systems for securely generating secret shares in a distributed manner and distributing those secret shares to cryptographic devices are disclosed. The cryptographic devices can use these secret shares to perform threshold distributed cryptographic operations (e.g., encryption and decryption). The cryptographic devices can be partitioned into groups based on the total number of devices and a threshold number. One generating device from each group can generate a secret share corresponding to that group, then transmit the secret share to members of the group. The generating devices can also generate commitments and transmit those commitments to other cryptographic devices. A group of confirming devices can use the commitments to generate confirmation values that can be used to confirm that the secret share were generated and distributed correctly. Later, a threshold number of cryptographic devices, collectively possessing all the secret shares can perform cryptographic operations using those secret shares.
H04L 9/06 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p.ex. système DES
One embodiment of the invention is directed to a method comprising: receiving, by a token requestor computer from a point of interaction device, verification of authentication data and the linking data; determining, by the token requestor computer, a token based on the linking data after analyzing the verification of the authentication data; transmitting, by the token requestor computer to a token service computer, a cryptogram request message; receiving, by the token requestor computer from the token service computer, a cryptogram associated with the token; generating, by the token requestor computer, an authorization request message comprising the token and the cryptogram to a processor computer; and receiving, by the token requestor computer, an authorization response message from the processor computer.
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
G06F 21/32 - Authentification de l’utilisateur par données biométriques, p.ex. empreintes digitales, balayages de l’iris ou empreintes vocales
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
98.
SECURE ON-DEMAND ULTRA-WIDEBAND COMMUNICATION CHANNELS SYSTEMS AND METHODS
A method includes forming a communication channel between a user device and an access device. The communication channel is then secured using a user device key pair in the user device and an access device ephemeral key pair in the access device. The access device then generates a session key using at least a private cryptographic key in the access device ephemeral key pair, and a public key in the user device key pair. The access device then uses the session key to secure an ultra-wideband communication channel between the user device and the access device.
H04L 9/30 - Clé publique, c. à d. l'algorithme de chiffrement étant impossible à inverser par ordinateur et les clés de chiffrement des utilisateurs n'exigeant pas le secret
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04W 12/04 - Gestion des clés, p.ex. par architecture d’amorçage générique [GBA]
H04W 12/63 - Sécurité dépendant du contexte dépendant de la proximité
H04W 12/122 - Contre-mesures pour parer aux attaques; Protection contre les dispositifs malveillants
Conducting secure transfers between computing devices can pose a challenge. Therefore, an oblivious transfer can be used to conduct a secure transfer. The oblivious transfer (OT) is an interactive protocol between two parties: a sender computing device and a receiver computing device. An OT protocol involves the sender computing device holding two messages m0 and m1, and the receiver computing device holding a bit b ? {0, 1}. At the end of the protocol, the receiver computing device should only learn the message mb and nothing about the other message m1?b, while the sender computing device should learn nothing about the bit b. With the steady progress in quantum computing, several post-quantum oblivious transfer protocols can be derived.
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
100.
SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR LEARNING CONTINUOUS EMBEDDING SPACE OF REAL TIME PAYMENT TRANSACTIONS
Methods, systems, and computer program products for learning continuous embedding space of real time payment (RTP) transactions are provided. A method may include receiving RTP data including a plurality of attributes, including a sender and a receiver. One attribute is selected as a target attribute. The remaining attributes are input into a first machine learning model (e.g., NLP model), including at least one embedding layer and one hidden layer, which is trained to predict the target attribute. After the model is trained, each of the remaining attributes are converted to a first vector using the at least one embedding layer of the machine learning model to form a first set of vectors. The first set of vectors are stored and subsequently input into a second machine learning model to perform at least one second task different than the first task.
G06F 7/08 - Tri, c. à d. rangement des supports d'enregistrement dans un ordre de succession numérique ou autre, selon la classification d'au moins certaines informations portées sur les supports
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
G06T 11/20 - Traçage à partir d'éléments de base, p.ex. de lignes ou de cercles
G06F 16/00 - Recherche d’informations; Structures de bases de données à cet effet; Structures de systèmes de fichiers à cet effet
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