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.
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
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 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
5.
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
9.
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
12.
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
14.
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
19.
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
23.
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
29.
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
34.
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
39.
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
41.
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
42.
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
45.
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
47.
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
49.
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
50.
SYSTEM AND METHOD FOR EFFICIENTLY MANAGING CALLOUTS
A method of using a processing computer comprising a memory comprising a hash index table and an array index table is disclosed. The method includes receiving an initial request message comprising a plurality of data fields with data elements for a transaction, and creating service request messages, where each service request message comprises a transaction key and data elements. The method includes transmitting the service request messages to server computers, which process them and generate service response messages, each service response message having the transaction key and response data. The method includes receiving the service response messages. The method includes for each of the service response messages: accessing the hash index table and determining a row address identifier for a row in the array index table based on the transaction key, and accessing data in the row of the array index table associated with the row address identifier.
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
51.
EMBEDDING COMPRESSION FOR EFFICIENT REPRESENTATION LEARNING IN GRAPH
A method performed by a server computer is disclosed. The method comprises generating a binary compositional code matrix from an input matrix. The binary compositional code matrix is then converted into an integer code matrix. Each row of the integer code matrix is input into a decoder, including plurality of codebooks, to output a summed vector for each row. The method then includes inputting a derivative of each summed vector into a downstream machine learning model to output a prediction.
Provided are systems for tuning prediction results of a machine learning model that include at least one processor to determine a plurality of values associated with a prediction matrix based on an output of a trained machine learning model, tune a set of reference measures to provide an adjustment to a predicted classification value of a prospective output of the trained machine learning model, apply the set of reference measures to determine a predicted classification value of a real-time output of the trained machine learning model, wherein the output of the trained machine learning model comprises a predicted classification value for a real-time event. Methods and computer program products are also provided.
Systems, methods, and computer program products that obtain a plurality of features associated with a plurality of samples and a plurality of labels for the plurality of samples; generate a plurality of first predictions for the plurality of samples with a first machine learning model; generate a plurality of second predictions for the plurality of samples with a second machine learning model; generate, based on the plurality of first predictions, the plurality of second predictions, the plurality of labels, and a plurality of groups of samples of the plurality of samples; determine, based on the plurality of groups of samples, a first success rate associated with the first machine learning model and a second success rate associated with the second machine learning model; and identify, based on the first success rate and the second success rate, a weak point in the machine learning first model or the second model.
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 subsequently use these secret shares to perform threshold distributed cryptographic operations (such as encryption or decryption). A threshold number of generating cryptographic devices can each generate their own secret shares. These devices can also each generate partial secret shares that can be combined by receiving cryptographic devices to generate their own respective secret shares. Additionally, the generating devices can generate commitments corresponding to their secret shares. The generating devices can transmit the commitments to the other cryptographic devices and the partial secret shares to their corresponding receiving devices. At a later time, cryptographic devices possessing at least a threshold number of secret shares can collectively 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
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
55.
SYSTEM AND METHODS FOR ENABLING ULTRA-WIDE BAND IN PASSIVE DEVICES
Techniques for enabling usage of an Ultra-Wideband (UWB) chip on a passive device are disclosed. The passive device comprises a substrate including a first electronic component, and a second electronic component. The first electronic component is programmed to communicate with an access device using a first communication protocol, and the second electronic component is programmed to communicate with the access device using a second wireless communication protocol. The passive device includes a first antenna electrically coupled to at least the first electronic component or the second electronic component, and a second antenna electrically coupled to at least the second electronic component. The first antenna is adapted to receive a first signal from the access device, which powers at least the second electronic component, thereby causing the second electronic component to cause the second antenna to emit a second signal that is received by the access device.
H04B 1/401 - Circuits pour le choix ou l’indication du mode de fonctionnement
H04B 1/50 - Circuits utilisant des fréquences différentes pour les deux directions de la communication
H04B 1/3816 - TRANSMISSION - Détails des systèmes de transmission non caractérisés par le milieu utilisé pour la transmission Émetteurs-récepteurs, c. à d. dispositifs dans lesquels l'émetteur et le récepteur forment un ensemble structural et dans lesquels au moins une partie est utilisée pour des fonctions d'émission et de réception avec des connecteurs pour programmer des dispositifs d’identification
H04B 5/00 - Systèmes de transmission à induction directe, p.ex. du type à boucle inductive
H04B 5/02 - Systèmes de transmission à induction directe, p.ex. du type à boucle inductive utilisant un émetteur-récepteur
H04W 4/80 - Services utilisant la communication de courte portée, p.ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
H01Q 5/25 - Systèmes à ultralarge bande, p.ex. systèmes à résonnance multiple; Systèmes à impulsions
H01Q 1/22 - Supports; Moyens de montage par association structurale avec d'autres équipements ou objets
56.
SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR DETECTING MERCHANT DATA SHIFTS
Systems, methods, and computer program products for detecting merchant data shifts may identify a shift in transaction volume of a merchant system across Merchant Category Codes (MCCs) using a combination of time series analysis and machine learning; wherein obtaining, with at least one processor, historical transaction data associated with a time series of a plurality of historical transactions at a merchant system over a historical period of time, the historical transaction data including a plurality of merchant category codes (MCCs) associated with the plurality of historical transactions; applying, with the at least one processor, a difference transform to the historical transaction data to generate transformed data.
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
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
One or more surface features (e.g., capacitive buttons, fingerprint sensor) may be exposed on a surface of a card (e.g., chi payment card). The card may store multiple applications/accounts of a user. The card receives a selection of one of the accounts by the user placing a finger on or pressing on a surface feature associated with the selected account. The card provides credentials associated with the selected account to a terminal. The multi-application card may disable credentials associated with the remaining accounts thereby appearing as a single-application card to the terminal during a transaction.
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 19/073 - Dispositions particulières pour les circuits, p.ex. pour protéger le code d'identification dans la mémoire
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
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
G06F 3/044 - Numériseurs, p.ex. pour des écrans ou des pavés tactiles, caractérisés par les moyens de transduction par des moyens capacitifs
58.
MOBILE DEVICE APPLICATION FOR ACCOUNT SELECTION ON MULTI-ACCOUNT CARD
Embodiments provide an NDEF interface on a co-badged user card (e.g., a payment card storing multiple payment applications or accounts) to modify the payment application selection status on the co-badged card using a mobile application provided on a user device with NDEF support. The NDEF interface on the co-badged card allows communication with the mobile application stored on the user device operating a variety of operating systems (e.g., iOS and Android).
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
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
Provided are a system, method, and computer program product for secure payment device data storage and access. The method includes storing payment device data associated with a payment device of a user and generating a unique uniform resource locator (URL) associated with the payment device. The method also includes transmitting the unique URL to an application provider system through a first communication channel and receiving a data access request from the client device via the unique URL through a second communication channel separate from the first communication channel. The method further includes, in response to receiving the data access request, verifying an identity of the user by executing a step-up authentication protocol. The method further includes, in response to verifying the identity of the user, transmitting a data access response including the payment device data to the client device through the second communication channel.
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
G06Q 20/00 - Architectures, schémas ou protocoles de paiement
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 40/00 - Finance; Assurance; Stratégies fiscales; Traitement des impôts sur les sociétés ou sur le revenu
H04L 9/16 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité utilisant plusieurs clés ou algorithmes qui sont changés pendant l'opération
60.
SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR HOST BASED PURCHASE RESTRICTION
Systems, methods, and computer program products that receive, from a merchant system, an authorization request associated with a transaction at the merchant system using a chip-based payment device storing a chip-based purchase restriction in a chip-card format, the authorization request including a purchase restrictions flag indicating whether the merchant system supports host-based purchase restrictions; determine, based on the purchase restrictions flag, that the merchant system supports host-based purchase restrictions; and transmit, to the merchant system, an authorization response associated with the transaction, wherein the authorization response includes a field including a host-based purchase restriction in the same chip-card format that the chip-based purchase restriction is stored on the chip-based payment device, and wherein the host-based purchase restriction is configured to cause the merchant system to override the chip-based purchase restriction with the host-based purchase restriction for processing the transaction.
A method, performed by a digital identity computer, for processing a resource request is disclosed. The method includes receiving, from a user device operated by a user, a resource request and indication of identity attributes needed to process the resource request. The digital identity computer may then retrieve an identity token associated with the user and compute an authentication score based on the sensitivity and rarity of the identity attributes indicated. The authentication score can be used to determine an authentication process. After determining and executing the authentication process with the user device, the digital identity computer may then grant the user device access to the resource requested.
A method is disclosed. The method includes processing a group interaction request for an interaction involving a group. Better assurance for the interaction is provided by providing a one-time password that has a number of portions that are sent to a plurality of user devices. The portions are received and one user device may concatenate the portions to form the one-time password. It may then be entered to authenticate the interaction. Other examples include the use of an authorization request message that is authorized for an initial value. Later, separate authorization request messages with different credentials may be transmitted for different users in the group.
G06F 21/46 - Structures ou outils d’administration de l’authentification par la création de mots de passe ou la vérification de la solidité des mots de passe
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 21/44 - Authentification de programme ou de dispositif
A method includes an access device determining an interaction value associated with an interaction. The access device prompts a user operating a user device for a secret. The access device receives the secret. The access device receives an initial communication then a user device certificate comprising a public key from the user device. The access device then verifies the certificate. The access device concatenates at least the secret and an unpredictable number to form a concatenated value. The access device encrypts the concatenated value with the public key, then transmits the encrypted concatenated value. The user device decrypts the encrypted concatenated value with a private key, verifies the unpredictable number, verifies the secret, determines whether or not the interaction is approved, produces an interaction authorization result, and then provides the interaction authorization result to the contactless access device. The access device receives the interaction authorization result.
Systems, methods, and computer program products for dynamic passcode communication use a merchant application installed on a user device that receives transaction data associated with a transaction at a merchant system. The transaction data may include an account identifier associated with an account at an issuer system. The merchant application determines, based on the account identifier, whether an issuer application associated with the issuer system is installed on the user device. In response to determining that the issuer application is installed on the user device, the merchant application transmits, to the issuer application, a request for a dynamic passcode. The merchant application receives, from the issuer application, the dynamic passcode and transmits, to the issuer system, an authorization request including the account identifier and the dynamic passcode. The merchant application receives, from the issuer system, an authorization response authorizing or denying the 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
A method is disclosed. The method includes providing, by an SDK and a first application in a mobile device, first and second security values to a security value verification module in the mobile device. If the mobile device confirms that the first and second security values match, then a second application can proceed with interaction processing.
A system, method, and computer program product are provided for consent management. A method may include receiving a first data request for user data associated with a user, the user data stored in a user data database; communicating a consent request to the requester system; receiving a consent response from the requester system; storing consent data associated with the consent response for the user data requested in the first data request in an immutable ledger; receiving a consent verification request from the user data database, the consent verification request based on a second data request for the user data from the requester system to the user data database; verifying the consent verification request based on the consent data; and communicating a consent verification response to the user data database, the consent verification response indicating consent from the user to share the user data with the requester system.
A method for performing a key recovery process is disclosed. The method comprises entering, in a user device, a user identifier unique to a user. The user device may then obscure the user identifier to form an obscured user identifier. The user device may then transmit the obscured user identifier to a first and second entity computer. The method may then include the first entity computer generating a first output using the obscured user identifier and a first share, and the second entity computer generates a second output using the obscured user identifier and a second share. As a response to transmitting the obscured identifier, the user device may receive the first output from the first entity computer and the second output from the second entity computer. The user device may then generate a secret key after processing the first output and the second output, completing the key recovery process.
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
68.
SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR SECURING AUTHORIZATION COOKIES AND ACCESS TOKENS
Systems, methods, and computer program products: determine, a network delay equal to a server system time stamp associated with a system time of a server at which a login request from a user device is received by the server minus a first server system time stamp received in the login request; initiate a session timer from a time equal to the first user system time stamp plus the network delay; transmit, to the user device, an authentication cookie or access token; receive, from the user device, a further request including the authentication cookie or access token and a user system time stamp associated with the system time of the user device; validate, the authentication cookie or access token; determine, a time difference between the user system time stamp plus the network delay and the session timer; and authorize or deny, based on the time difference, the further request.
G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p.ex. pour le traitement simultané de plusieurs programmes
One embodiment of the present disclosure may include a method for providing access to resources by a resource provider computer during a first web session with a client device. The resource provider can provide a first web page to the client device, the first web page including a first option to complete a first access request with the resource provider computer as a guest for access to a first resource. The resource provider computer can receive a first selection of the first option from the client device and provide a second web page including a second option to remember the user device. The resource provider computer can, in response to receiving a second selection of the second option from the client device, send a remember flag to a server computer. The resource provider computer can then receive a recognition identifier from the server computer and store the recognition identifier.
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
H04L 67/146 - Marqueurs pour l'identification sans ambiguïté d'une session particulière, p.ex. mouchard de session ou encodage d'URL
G06Q 10/08 - Logistique, p.ex. entreposage, chargement ou distribution; Gestion d’inventaires ou de stocks
G06Q 20/12 - Architectures de paiement spécialement adaptées aux systèmes de commerce électronique
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
70.
METHOD AND SYSTEM FOR A FRAMEWORK FOR MONITORING ACQUIRER CREDIT SETTLEMENT RISK
Provided is a system, method, and computer program product for a framework for monitoring acquirer credit settlement risk. A system for monitoring acquirer credit settlement risk includes a transaction database and at least one processor. The processor may be programmed or configured to generate a first acquirer risk score based on a plurality of transaction records and a first risk algorithm, a first merchant risk score based on the plurality of transaction records and the first risk algorithm, a second acquirer risk score based on the plurality of transaction records and a second risk algorithm, and a second merchant risk score based on the plurality of transaction records and the second risk algorithm. A final acquirer risk score may be generated based on the first acquirer risk score, the second acquirer risk score, the first merchant risk score, and the second merchant risk score.
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 20/00 - Architectures, schémas ou protocoles de paiement
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 system includes a processor and a non-transitory computer readable medium coupled to the processor. The non-transitory computer readable medium includes code, that when executed by the processor, causes the processor to receive input from a user of a user device to generate an optimal payment location on an application display, generate a first boundary of the optimal payment location on the application display of the user device based upon a first motion of a payment enabled card in a first direction and generate a second boundary of the optimal payment location on the application display of the user device based upon a second motion of the payment enabled card in a second direction. The first boundary and the second boundary combine to form defining edges of the optimal payment location.
G06K 7/00 - Méthodes ou dispositions pour la lecture de supports d'enregistrement
G06K 7/08 - Méthodes ou dispositions pour la lecture de supports d'enregistrement avec des moyens de perception des modifications d'un champ électrostatique ou magnétique, p.ex. par perception des modifications de la capacité entre des électrodes
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
G06Q 20/00 - Architectures, schémas ou protocoles de paiement
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
72.
METHOD AND SYSTEM FOR ADVERSARIAL TRAINING AND FOR ANALYZING IMPACT OF FINE-TUNING ON DEEP LEARNING MODELS
Methods for adversarial training and/or for analyzing the impact of fine- tuning on deep learning models may include receiving a deep learning model comprising a set of parameters and a dataset of samples. A respective noise vector for a respective sample may be generated based on a length of the sample and a radius hyperparameter. For a target number of steps, the following may be repeated: adjusting the noise vector based on a step size hyperparameter, and projecting the respective noise vector to be within a boundary. The parameters of the deep learning model may be adjusted based on a gradient of a loss based on the noise vector. This may be repeated for each sample of the plurality of samples. A system and computer program product are also disclosed.
A method performed by a user device is disclosed. The method comprising generating a secret and measuring a biometric template of a user operating the user device. The method then generates a plurality of secret shares of the secret and of the biometric template. The user device then transmits the secret shares of the secret and of the biometric template to a plurality of recovery devices. After, the user device may then initiate a recovery of the secret and measure a biometric measurement of the user. Data of the biometric measurement may be transmitted to the plurality of recovery devices, where the recovery devices perform a partial computation. The user device use the plurality of partial computations to determine a match between the biometric template and the biometric measurement. If the two biometrics match, the user device can reconstruct the secret using shares of the secret from the recovery devices.
A method is disclosed. The method includes receiving by a tokenization server, a request to process an interaction from a user device, where the request includes a user identifier associated with a user. The tokenization server generates a first token using a first one-way cryptographic hash function based on the user identifier, and a second token using a second one-way cryptographic hash function based on the first token. The tokenization server retrieves first information stored in a first data storage associated with the tokenization server based on the second token, and transmits the first token and the first information to a processing computer. The processing computer is programmed to retrieve, from a second data storage associated with the processing computer, second information based on the first token, and execute the interaction based on the first information and the second information.
A method is disclosed. The method includes generating, by a first user device in association with a second user device, a second secret key on the second user device. The second secret key is derived from a first secret held by the first user device. The method includes generating a first commitment, transmitting, to the second user device, the first commitment, receiving, from the second user device, a second commitment, receiving, from the second user device, a random value and a ciphertext. The ciphertext is generated using the first commitment, the second commitment, and the random value. The method also includes verifying the ciphertext, and in response to verifying the ciphertext, modifying a group to include the second user 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
A method is disclosed. The method comprises a receiving a plurality of key-value pairs. The method then generates a random binary matrix of at least weight three. The random binary matrix has a number of non-zero binary values equal to the weight in each row. The method can then assign each key in the plurality of key-value pairs to a row in the random binary matrix. A key matrix can then be generated by appending a dense binary matrix to the random binary matrix. The method can then process the key matrix to output an encoding vector that encodes the values of the plurality of key-value pairs.
Provided is a system for segmenting large scale datasets according to machine learning models based on transfer learning that includes at least one processor programmed or configured to train a base machine learning model using a training dataset to generate a trained machine learning model, evaluate the trained machine learning model using an evaluation dataset, wherein, when evaluating the trained machine learning model using the evaluation dataset, the at least one processor is programmed or configured to generate a confidence score for each data instance of the evaluation dataset with the trained machine learning model, augment the evaluation dataset based on the confidence score for each data instance of the evaluation dataset to generate an augmented evaluation dataset, and retrain the trained machine learning model using the augmented evaluation dataset to generate a final machine learning model. Methods and computer program products are also provided.
Systems and methods for securing data transmissions using distance measurements are disclosed. A mobile device (such as a smart phone) and a base station can use ultra-wideband technology to determine the distance between the two devices. The distance measurements produced by the mobile device and the base station can be compared, directly or indirectly by the mobile device, the base station, and/or an access device to determine whether the mobile device is present at an access device or if the mobile device is not present at the access device (as expected during a relay attack). If the mobile device is not present at the access device, the access device can prevent or cancel an interaction based on the data transfer (e.g., opening a locked door of a secure building in response to receiving an access credential from the mobile device).
A method is disclosed and includes receiving a token request message comprising a real credential or a reference to the real credential, a unique value, and action data from a token requestor, and obtaining a token. The method also includes transmitting the token to the token requestor, and providing the unique value and the action data to a data matching computer. The data matching computer stores the unique value and the action data. The data matching computer subsequently receives a message from a data processing computer comprising the unique value, determines that the received unique value matches the stored unique value, and provides the action data to the data processing computer or performs an action with respect to the action data.
A method includes a computer receiving a request to conduct an interaction from a mobile device. The computer obtains a computer address and provides the computer address to the mobile device. The mobile device provides an access request to the computer address, and the access request is thereafter routed to an identity provider computer. The identity provider computer identifies identity data associated with the mobile device or a user of the mobile device. The computer obtains the identity data or a derivative of the identity data from the identity provider computer. The computer determines if the identity data or the derivative of the identity data matches previously stored identity data or a previously stored derivative of identity data. If a match is determined, the computer provides a list of user device identifiers to the mobile device.
G06F 21/45 - Structures ou outils d’administration de l’authentification
G06F 21/30 - Authentification, c. à d. détermination de l’identité ou de l’habilitation des responsables de la 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
Provided is a system for analyzing features associated with entities using an embedding tree, the system including at least one processor programmed or configured to receive a dataset associated with a plurality of entities, wherein the dataset comprises a plurality of data instances for a plurality of entities. The processor may be programmed or configured to generate at least two embeddings based on the dataset and determine split criteria for partitioning an embedding space of at least one embedding tree associated with the dataset based on feature data associated with an entity and embedding data associated with the at least two embeddings. The processor may be programmed or configured to generate at least one embedding tree having a plurality of nodes based on the split criteria. Methods and computer program products are also provided.
Provided is a system for detecting an anomaly in a multivariate time series that includes at least one processor programmed or configured to receive a dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, determine a set of target data instances based on the dataset, determine a set of historical data instances based on the dataset, generate, based on the set of target data instances, a true value matrix, a true frequency matrix, and a true correlation matrix, generate a forecast value matrix, a forecast frequency matrix, and a forecast correlation matrix based on the set of target data instances and the set of historical data instances, determine an amount of forecasting error, and determine whether the amount of forecasting error corresponds to an anomalous event associated with the dataset of data instances. Methods and computer program products are also provided.
A computer obtains node embeddings, node periodicity classifications, edge embeddings, and edge periodicity classifications for each time of a time period. The computer determines subgraph embeddings based on a subgraph of the graph, times in the time period, the node embeddings for nodes in the subgraph, the edge embeddings for edges in the subgraph, the node periodicity classifications for the nodes in the subgraph, and the edge periodicity classifications for the edges in the subgraph. The computer translates each subgraph embedding of the subgraph embeddings for each time of the time period into projected subgraph embeddings. For the subgraph, the computer aggregates the plurality of projected subgraph embeddings into an aggregated subgraph embedding. The computer determines if the subgraph is periodic based upon at least the aggregated subgraph embedding.
A method is disclosed. The method comprises determining a time series, a subsequence length. The length of the time series may then be determined, and an initial matrix profile may then be computed. The method may then form a processed matrix profile for a first subsequence of the subsequence length by applying the first subsequence to the initial matrix profile. A second subsequence may then be determined from the processed matrix profile. The method may then include comparing the second subsequence to other subsequences in a dictionary and adding it to the dictionary. The subsequences in the dictionary may be used to generate a plurality of subsequence matrix profiles. The method may then include forming an approximate matrix profile using the plurality of subsequence matrix profiles and then determining one or more anomalies in the time series or another time series using the approximate matrix profile.
Provided are systems, methods, and computer program products for generating node embeddings. The system includes at least one processor programmed or configured to generate a graph comprising a plurality of nodes, generate an embedding for each node of the plurality of nodes, each embedding comprising at least one polar angle and a vector length. store each embedding of a plurality of embeddings in memory, and in response to processing the graph with a machine-learning algorithm, convert at least one embedding of the plurality of embeddings to Cartesian coordinates.
Provided is a method for normalizing embeddings for cross-embedding alignment. The method may include applying mean centering to the at least one embedding set, applying spectral normalization to the at least one embedding set, and/or applying length normalization to the at least one embedding set. Spectral normalization may include decomposing the at least one embedding set, determining an average singular value of the at least one embedding set, determining a respective substitute singular value for each respective singular value of a diagonal matrix, and/or replacing the at least one embedding set with a product of the at least one embedding set, a right singular vector, and an inverse of the substitute diagonal matrix. The mean centering, spectral normalization, and/or length normalization may be iteratively repeated for a configurable number of iterations. A system and computer program product are also disclosed.
Embodiments of the present disclosure are directed to methods for multi-party fixed point multiplication. The methods can include replicated methods for multi-party fixed point multiplication where the inputs and output are represented using replicated secret sharing. One replication method can require only a single round of communication in the online phase and is secure against a semi-honest adversary. Another replication method can require may include an additional key to identify any malicious communicating parties. The methods can also include a Shamir sharing fixed point multiplication method and an additive secret sharing fixed point multiplication method.
Methods and systems for quickly and accurately training machine learning models to perform tasks related to new contexts are disclosed, particularly new contexts for which there is little available training data. One or more source data sets can be divided into a plurality of source sub-sets, which can be used to train a plurality of source sub-models. From this plurality of source sub-models, an estimate parameter set can be determined. The estimate parameter set and a target data set (which may comprise a comparatively small number of data elements) can be used to train a target model to perform some task. In this way, the source data set can be leveraged to train the target model. Additionally disclosed is a novel method of generating color maps, image representations of non-image data. These color maps can be used to train the sub-models and target model, improving model performance.
Provided are a system, method, and computer program product for an account-to-account transaction network. The method includes receiving, via a first application programming interface (API), a transaction request for a transaction including a transaction amount, an issuer identifier, and a merchant identifier. The method includes determining an issuer uniform resource locator (URL) based on the issuer identifier, generating an intent identifier associated with the transaction request, and transmitting the issuer URL and the intent identifier via the first API. The method includes transmitting the intent identifier, the transaction amount, and the merchant identifier to an issuer system associated with the issuer identifier via a second API. The method includes, in response to receiving an authenticated consent identifier from the issuer system, determining a merchant account identifier and an issuer account identifier, and transmitting a combined authorization and settlement message to cause settlement for the transaction amount.
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
90.
SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR ANALYZING MULTIVARIATE TIME SERIES USING A CONVOLUTIONAL FOURIER NETWORK
Provided is a system for analyzing a multivariate time series that includes at least one processor programmed or configured to receive a time series of historical data points, determine a historical time period,, determine a contemporary time period, determine a first time series of data points associated with a historical transaction metric from the historical time period, determine a second time series of data points associated with a historical target transaction metric from the historical time period, determine a third time series of data points associated with a contemporary transaction metric from the contemporary time period, and generate a machine learning model, wherein the machine learning model is configured to provide an output that comprises a predicted time series of data points associated with a contemporary target transaction metric. Methods and computer program products are also provided.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p.ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
A method includes receiving, from a first transfer application on a first user device associated with a first user, a transfer request message for a transfer transaction. The transfer request message comprises a digital tag associated with a second user and a transaction amount. The method includes generating and transmitting a code to a second transfer application on a second user device, and receiving a confirmation message that the second user wants to conduct the transfer transaction and is sharing the code with the first user. The method includes transmitting the code to the first transfer application on the first user device. The first transfer application compares the code received from the digital tag computer with the code received from the second user and initiates the transfer transaction if the code received from the digital tag computer with the code received from the second user match.
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
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
92.
SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR STATE COMPRESSION IN STATEFUL MACHINE LEARNING MODELS
Described are a system, method, and computer program product for state compression in stateful machine learning models. The method includes receiving a transaction authorization request for a transaction and loading at least one encoded state of a recurrent neural network (RNN) model from a memory. The method further includes decoding the at least one encoded state by passing each encoded state through a decoder network to provide at least one decoded state. The method further includes generating at least one updated state and an output for the transaction by inputting at least a portion of the transaction authorization request and the at least one decoded state into the RNN model. The method further includes encoding the at least one updated state by passing each updated state through an encoder network to provide at least one encoded updated state, and storing the at least one encoded updated state in the memory.
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 system includes a processor and a non-transitory computer readable medium coupled to the processor. The non-transitory computer readable medium comprises code that when executed by the processor, causes the processor to receive a money transfer amount indicative of an amount of funds to be transferred to a recipient. The processor generates a key code associated with the money transfer amount that is provided to a user of the system and given to the recipient by the user. The key code is programmed by the processor to enable the recipient to use the key code to redeem the amount of funds associated with the money transfer amount.
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de banque à domicile
G06Q 20/00 - Architectures, schémas ou protocoles de paiement
G07F 7/02 - Mécanismes actionnés par des objets autres que des pièces de monnaie pour déclencher ou actionner des appareils de vente, de location, de distribution de pièces de monnaie ou de papier-monnaie, ou de remboursement par des clés ou d'autres dispositifs enregistrant un crédit
G06Q 40/00 - Finance; Assurance; Stratégies fiscales; Traitement des impôts sur les sociétés ou sur le revenu
G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p.ex. pour le traitement simultané de plusieurs programmes
A method is disclosed. The method comprises receiving, from a server computer, a challenge, and displaying objects from an object list to a user. The method includes determining that a user has visually selected an object from the object list and moving the selected object on a display according to screen coordinates. A client computer captures a biometric of the user, and compares the biometric to another biometric stored in the client computer to provide a first comparison output, and compares a derivative of the selected object to a derivative of an object stored in the client computer to produce a second comparison output. The client computer signs the challenge with a private key and sends the signed challenge to the server computer, and the server computer verifies the signed challenge.
G06F 21/32 - Authentification de l’utilisateur par données biométriques, p.ex. empreintes digitales, balayages de l’iris ou empreintes vocales
G06F 21/45 - Structures ou outils d’administration de l’authentification
G06F 21/33 - Authentification de l’utilisateur par certificats
G06V 40/18 - Caractéristiques de l’œil, p.ex. de l’iris
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
95.
OBLIVIOUS TRANSFER FROM KEY ENCAPSULATION MECHANISMS
Embodiments can perform efficient OT (oblivious transfer) protocols to efficiently establish OT correlations that could be used for an MPC protocol. The present embodiments relate to a non-interactive OT (NIOT) protocol using a key encapsulation mechanism (KEM). Two OT protocols are non-interactive OTs, in which a sender generates private, public key pair (pk, sk) that is independent of its input or generated OT correlations. The two OT protocols use a cryptographic hash function and a one-way secure dense key encapsulation mechanism (KEM).
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 9/06 - 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.
SCALABLE NEURAL TENSOR NETWORK WITH MULTI-ASPECT FEATURE INTERACTIONS
A method includes determining a set of regions for each of a first plurality of images of a first item type, a second plurality of images of a second item type, and a third plurality of images of a third item type. The method also includes for each region in each set of regions of the images, generating, by the processing computer, a vector representing the region, and then generating a plurality of aggregated messages using the vectors corresponding to combinations of images of different types of items, the images being from the first, second, and third plurality of images. Then, unified embeddings are generated for the images in the first, second, and third plurality of images, respectively, using aggregated messages in the plurality of aggregated messages. Matching scores associated with combinations of the images are created using the unified embeddings and a machine learning model.
Provided are a system, method, and computer program product for network anomaly detection. The method includes receiving event data associated with a plurality of events in a computer network. The method also includes determining nested groups of the event data representing tiers of an operational hierarchy. The method further includes generating display data to show a graphical representation of the event including a plurality of nested graphical nodes and at least one spline. Each graphical node is associated with a group or a computer node, each graphical node encompasses and/or is encompassed by another graphical node, a size of each graphical node is proportional to an aggregated parameter value of events associated therewith, each spline connects at least two graphical nodes and includes a curve that passes through a common graphical node, and each spline is associated with a communication between at least two computer nodes.
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p.ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comport
98.
METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR PROTOCOL PARSING FOR NETWORK SECURITY
Provided is a method for protocol parsing for network security. The method may include receiving, by a packet capture system, a plurality of packets, parsing lower layer data from each packet, and communicating a respective payload of each respective packet to at least one first queue. A routing system may route the respective payload of each respective packet to a respective second queue of a plurality of second queues based on a respective protocol of the respective packet. A respective protocol parser node of a parsing system may parse higher layer data from the respective payload of each respective packet from each respective second queue. The packet capture system may communicate the lower layer data for each packet to a third queue, and the parsing system may communicate the higher layer data for each packet to the third queue. A system and computer program product are also disclosed.
H04L 51/21 - Surveillance ou traitement des messages
H04L 51/00 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel
Embodiments are directed to methods and systems for crypto-agile encryption and decryption. A computer system can possess a protocol file that identifies one or more cryptographic software modules. Using these cryptographic software modules, the computer system can generate a plurality of shared secrets and a session key, then use the session key to encrypt a message. The message can be sent to a server computer that can subsequently decrypt the message. At a later time, the protocol file can be updated to identify a different set of cryptographic software modules, which can be used to encrypt messages. Further, the server computer can transmit additional cryptographic software modules to the computer system, enabling the computer system to use those cryptographic software modules to generate cryptographic keys. As such, the cryptographic protocol file can be changed in response to changes in the cryptographic needs of the computer system.
Data encryption keys (and other sensitive data) can be secured during use by a key protection service that performs cryptographic operations on behalf of a client application. The key protection service can be implemented as a lightweight virtual machine that appears externally as a container and that can be executed in a secured environment. The lightweight virtual machine can include containerized processes to support an application program interface to interact with the client application and an attestation client to interact with a secured key storage system external to the secured environment.
G06F 9/455 - Dispositions pour exécuter des programmes spécifiques Émulation; Interprétation; Simulation de logiciel, p.ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
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
brevets
