70 results about "Identity resolution" patented technology

The invention discloses a data sharing and tracing method and system based on a block chain, and relates to the technical field of block chains and distributed identifier analys.The method comprises the steps that a global unique identifier is allocated to a digital object through a data networking identifier analysis system, and a metadata mapping relation between the global unique identifier and the digital object is recorded into a block chain network; when a data sharing request is responded, calling an identifier analysis system to position a target digital object, verifying the authority of a requester, recording an access event, generating transaction data, and writing the transaction data into a block chain network; and according to the transaction data recorded in the block chain network, constructing a full-life-cycle tracing chain of the digital object, querying the circulation history of the digital object from the tracing chain according to a time sequence, an operation type or a participant, and verifying the data integrity of the digital object. According to the method, the accuracy and efficiency of data positioning are improved, the security and controllability of data sharing are enhanced, and efficient query of data circulation history and accurate positioning of tampering behaviors are guaranteed.

Identity nodes within bidirectional dual node hashchain (BDNH) bundles generate hash keys by processing partial metadata and linking them through identity graphs. Translation nodes perform identity resolution, data anonymization, and re-keying by transforming original identifiers into anonymous IDs and re-key IDs. Laplace noise enabler capsules introduce Laplace noise into translation layer logs for differential privacy. BDNHs share metadata between adjacent bundles for secure and efficient data transmission. A cognitive analytics layer shared among all bundles verifies hashed encrypted data packets using a gossip protocol for real-time validation. An analytics workspace layer activates re-key IDs and creates digital tags for datasets using BERT transformers, linking data through knowledge graphs. The system manages parallel processing and includes machine learning modules, homomorphic encryption, federated learning, zero-knowledge proofs, attribute-based encryption, and anomaly detection. Bundles' AI engines work with the Laplace noise enablers to analyze data, optimize noise, and enhance the data clean room functionality.

In various embodiments of the present invention, identity components such as name, date of birth, address, social security number, driver's license number etc. are transformed into vector representations which are then stored in a vector search database. In preferred embodiments, each vectorized identity component represents a column in a table which is implemented as a database and which is incorporated into a trained model. This trained model is then used on a real time or near real time basis to make identity verification and fraud detection decisions in connection with proposed “transactions”. These “transactions” can take various forms such as purchasing a good or service, opening an account, performing a background check and / or any other action wherein it is necessary or desirable to confirm that the person seeking to perform the transaction is who they say they are.

A system, method, and computer-program product includes receiving, from a third-party identity management system, an authentication response indicating a set of login attributes; obtaining, from an identity resolution service, a set of identity and authorization attributes using the set of login attributes, the set of identity and authorization attributes including a unique user identifier (UID); granting, by the identity resolution service, a session initiation token when the set of identity and authorization attributes satisfy predefined authorization criteria; in response to the identity resolution service granting the session initiation token: allocating a compute session and a persistent storage resource to the unique UID; executing, via the compute session, an operation that modifies files stored in the persistent storage resource; and transmitting, to a version control system, a version control operation that records the files modified in the persistent storage resource to a code repository using the unique UID or group identifiers.

A system and method for building first-party identity graphs using third-party data employs deep learning to enable privacy-preserving identity resolution. The system includes a deep learning model trained with transformer architecture and contrastive learning on a comprehensive third-party identity graph containing personally identifiable information (PII). Custom tokenizers process PII data by leveraging hierarchical structures and domain-specific characteristics to handle variations in spellings, abbreviations, and data formats. An identity matcher generates vector embeddings from first-party PII inputs, which are stored in a vector database for efficient similarity searches. A distributed similarity computation component compares first-party embeddings with third-party data embeddings, outputting similarity scores that enable fuzzy matching capabilities. A graph builder constructs accurate first-party identity graphs based on similarity thresholds while incorporating relevant third-party data.

The application provides a blockchain-based identity resolution performance improvement method, including the following steps: A, identity registration: S1, establish object identity; S2, build sub-chain division mechanism; S3, construct identity metadata and data template; S4, build access control mechanism based on data template and signature mechanism: 1) DID document data structure; 2) DID-based identity registration process and signature scheme; B, identity resolution: S1, build identity resolution mechanism and develop identity resolution interface. The application distinguishes sub-chains based on object DID identifiers, builds cross-enterprise unified coding, and realizes one object one code. It ensures that the identity data of the same object is stored on the same sub-chain, effectively reduces the number of access to the blockchain in the identity resolution process, and improves the identity resolution performance. The identity metadata management mechanism is introduced, the identity data template is built through the identity metadata, and the standardization of data during identity registration is realized through the data template.

The present invention relates to the field of ecological environment inspection and testing, and in particular to an ecological environment inspection and testing information management system based on industrial Internet identity resolution. The present invention realizes unified identification of environmental detection tasks, efficient data collection and transmission, intelligent spatiotemporal correlation analysis, and pollution source tracing prediction by introducing industrial Internet identity resolution technology, spatiotemporal graph convolutional network (ST-GCN), and a dynamic multi-path optimization algorithm of reinforcement learning.

This invention discloses a skeletonless, non-skeleton-dependent method for tracking similar small targets in UAV-based high-altitude overhead scenarios. Addressing the challenges of extremely low resolution, motion blur, and highly similar appearances of pedestrian targets in high-altitude aerial photography, which render traditional skeleton pose features ineffective, this method first dynamically calculates feature confidence by jointly evaluating the detection box size, detection confidence, and local sharpness. Based on this, it adaptively adjusts the fusion weights of spatial, appearance, and motion features in multi-source likelihood matching. Furthermore, to handle ambiguity regarding similar targets in congested scenes, it innovatively utilizes sharp displacements in a two-dimensional plane under UAV overhead views to construct motion pattern distinguishing factors (including direction and velocity consistency) to replace ineffective skeleton features and achieve accurate identity resolution. Finally, it combines multi-evidence fusion mechanisms such as spatiotemporal reachability, appearance consistency, and motion continuity to recover trajectories lost due to occlusion. This method effectively overcomes the feature instability and identity ambiguity problems of small targets under UAV high-altitude overhead views, significantly improving the robustness of target tracking in complex traffic scenarios.

The present invention provides a computing power trading method, device, and system, comprising: receiving a computing power resource application request sent by a target edge computing power node; generating a computing power contract, computing power billing, and computing power allocation information based on the computing power identifier and computing power resource requirements of the target edge computing power node; sending the computing power contract and computing power billing to the target edge computing power node and a computing power management platform; the target edge computing power node verifies the computing power contract and computing power billing and then sends a purchase request; receiving the purchase request sent by the target edge computing power node; sending computing power allocation information to the computing power management platform based on the purchase request; the computing power management platform allocates computing power resources to the target edge computing power node based on the computing power contract, computing power billing, and computing power allocation information. The method, device, and system can solve the problem that existing technologies for computing power trading for identity resolution calculations cannot ensure the credibility of computing power transactions.

The invention relates to a virtual bill exchange method and device, equipment and a storage medium, and the method comprises the following steps: carrying out the identity analysis of a virtual bill based on an encryption identifier on the virtual bill, and obtaining the identity feature data of the virtual bill; performing clause analysis based on the virtual bill identity feature data to obtain virtual bill clause information; performing quantitative evaluation on the potential exchange value of the virtual bill based on the virtual bill clause information to obtain a virtual bill value evaluation result; performing exchangeable resource matching on the virtual bill by using the virtual bill value evaluation result to obtain a matched exchangeable resource set; and performing exchange execution on the virtual bill according to the matched exchangeable resource set to obtain an exchange completion record, thereby solving the technical problem that the efficiency is low in the actual circulation and exchange process due to the fact that the traditional bill exchange mechanism is difficult to adapt to the digitalization, individuation and instantaneity characteristics of the virtual bill.

A multi-component system for identity resolution and customer segmentation utilizes bitmap encoding to overcome storage and processing limitations of traditional approaches. The system comprises a bitmap encoding processor that associates binary strings with user identities in an identity graph, where each bit corresponds to a unique segment and indicates user membership through bit state. A metadata association table stores descriptive information for each segment and dynamically links to the encoding processor for automatic updates. The binary strings serve dual purposes as both segment representations and user identifiers, eliminating separate identifier systems. The system can be implemented as a software development kit and deployed across various hardware architectures, making previously cost-prohibitive large-scale segmentation operations computationally feasible.

A system and method for improving identity resolution match rates using deep learning techniques that enable privacy-preserving fuzzy matching of personally identifiable information (PII) employs a deep learning model trained with transformer architecture and contrastive learning on third-party identity graph data. Custom tokenizers process different Pll types by leveraging hierarchical structures and domain-specific characteristics. The trained model generates vector embeddings that enable fuzzy matching to account for variations in spellings, typographical errors, and data inconsistencies without requiring adherence to strict data schemas. A vector database stores embeddings for nearest neighbor searches to identify potential identity matches based on distance calculations between embeddings. A match filter applies logic to determine valid matches, and the system can operate without requiring movement of raw Pll data from secure environments.

Methods and systems are provided for evaluating edges of collapsed identity graphs for identity resolution. In embodiments described herein, a collapsed state of identity graphs, such as based on an identity namespace limit being exceeded by the identity graphs, is determined by applying an identity node and edge of an incoming record to the identity graphs. A temporary state of the identity graphs is determined by pruning edges of the collapsed state. A non-collapsed state of the identity graphs that includes the edge of the incoming record is determined by applying the edge of the incoming record to the temporary state. A different edge is determined to be pruned from the non-collapsed state as when the different edge is applied to the temporary state with the edge of the incoming record, the temporary state collapses into the collapsed state. An identity graph is updated based on the non-collapsed state.

A system and method for scalable vector search in identity resolution uses deep learning to enable privacy-preserving matching of personally identifiable information (Pll). The system employs a deep learning model trained with transformer architecture and contrastive learning on third-party identity graph data. Custom tokenizers leverage hierarchical structures and domain-specific characteristics. The trained model generates vector embeddings that enable fuzzy matching, accounting for variations in spellings, typographical errors, and data inconsistencies. A scalable vector database stores embeddings for efficient nearest neighbor searches to identify potential identity matches. A distributed similarity computation component searches the vector database and outputs potential matches based on vector similarity. A match filter applies predefined criteria to refine results.

The embodiment of the invention discloses an unauthorized attack detection method and device, a storage medium and electronic equipment. According to the method, the access request of the user and the response data of the target system are collected through the aspect program, the identity information of the user is identified through the identity analysis capability provided by the aspect program in the application layer, and the permission configuration information of the user is determined in combination with the permission configuration of the specific target system, so that the requirements of different permission logic scenes can be flexibly adapted; and the method has universality. Simulating a user role based on user identity information and permission configuration information by using the large language model in combination with the semantic understanding capability of the large language model, and judging whether an access request of the user and a response data text of a target system contain sensitive information with unauthorized behavior characteristics or not; the method does not need to depend on strong rule definition of sensitive data, and has very good implicit sensitive data identification capability. The unauthorized attack detection device, the storage medium and the electronic equipment in the embodiment of the specification also have the above beneficial effects.

This application discloses a method and system for dynamic mapping and scheduling of network resources based on identity resolution, relating to the field of computer network technology. The method includes: acquiring request information of the task to be scheduled, multi-source identity signals, resource node status, overall network load status, and service preference information; fusing and parsing the multi-source identity signals to construct a cross-domain unified identity identifier and generate an identity attribute vector; then, combining computing resource status, network link status, and service preferences, calculating the identity adaptability between the unified identity identifier and each resource node to generate a candidate resource node list; finally, making dynamic scheduling decisions based on overall network load and link status to determine the target resource node and its corresponding network path, and issuing network configuration rules and resource scheduling instructions. This achieves collaborative perception and dynamic matching of identity, network, and computing resources, improving the accuracy, real-time performance, and service adaptability of cross-domain resource scheduling.

A system and method for privacy-preserving identity resolution using deep learning enables accurate matching of personally identifiable information (PH) while maintaining data security. The system employs a deep learning model trained with transformer architecture and contrastive learning on third- party identity graph data. Custom tokenizers leverage hierarchical structures and domain-specific characteristics. The trained model generates vector embeddings that enable fuzzy matching, accounting for variations in spellings, typographical errors, and data inconsistencies. A vector database stores embeddings for nearest neighbor searches to identify potential identity matches. The system operates without requiring strict data schemas and supports flexible handling of future data variations.

The application provides a Bluetooth random address generation method and related electronic equipment. The method is applied to an electronic device including a Bluetooth Host and a Bluetooth module. The method includes: the Bluetooth Host generating an identity resolution key; in the case that the electronic device meets a first condition, the Bluetooth Host sending the identity resolution key to the Bluetooth module; the first condition is a condition for triggering the electronic device to reduce power consumption; the Bluetooth module generating a resolvable private address RPA based on the identity resolution key IRK and periodically updating the RPA based on the IRK; in the case that the Bluetooth Host is to perform Bluetooth service, the Bluetooth Host sending an RPA request to the Bluetooth module; the Bluetooth module sending the latest updated RPA to the Bluetooth Host; and the Bluetooth Host performing Bluetooth service based on the latest updated RPA.

A cognition-native semantic execution platform is disclosed, comprising a modular architecture for executing memory-bearing semantic agents across centralized, decentralized, and heterogeneous computing environments. The system includes interoperable layers for agent modeling, memory-native substrate transport, scoped trust zone governance, dynamic identity resolution, and runtime ethical enforcement. Semantic agents carry structured fields including intent, context, memory, policy references, mutation descriptors, and lineage, enabling persistent identity, traceable behavior, and ethically governed semantic mutation. Trust validation is performed via entropy-resolved behavioral slope authentication rather than persistent credentials. Execution is policy-scoped and auditable through agent-embedded lineage graphs and cryptographically signed policy references. The platform supports fallback propagation in stateless environments and operates independently of transport topology, enabling scalable, transparent, and ethically constrained reasoning across distributed systems.

The invention discloses an intelligent data analysis and prediction method and system, and relates to the technical field of data analysis, and the method comprises the steps: collecting a real-time data stream and historical data, carrying out the identification analysis of the real-time data stream and historical data, and building a space-time correlation index; constructing a dynamic data view, and fusing a real-time data stream and historical data through a mapping layer to form a time series data matrix; sampling time point data are extracted through an adaptive sampling algorithm; inputting the sampling time point data into a neural symbol inference engine, extracting hidden layer features through a deep neural network, and performing inference in combination with a symbol logic rule base to generate a prediction rule set; and a multi-dimensional prediction result and a confidence interval are calculated, encryption gradient information is exchanged among multiple data holders through a federated learning framework, and prediction model parameters are updated. According to the method, efficient, interpretable and privacy-protected intelligent data analysis and prediction are realized, and the data security compliance requirement is met while the analysis quality is ensured.

The invention relates to the technical field of online transactions, in particular to a multi-dimensional task verification configuration method and system for identification analysis, and the method comprises the steps: recognizing the verification dimension type of a to-be-verified transaction task through analyzing the cross-chain scene and credit data dynamics of the to-be-verified transaction task; acquiring required verification configuration data according to the type; determining a corresponding dynamic reputation anchoring and cross-chain collaborative verification rule in combination with the dimension type; and when the matching relationship between the verification configuration data and the preset configuration standard meets the verification rule, completing multi-dimensional verification configuration. Wherein the preset configuration standard and the verification dimension type have a one-to-one correspondence mapping relationship. According to the method, scene self-adaption of the verification strategy is realized, the accuracy and efficiency of credit verification in a cross-chain environment are improved, dynamic and refined identity and credit management is supported, and the security and expandability of an online transaction system are enhanced.

An apparatus configured to process an indication of a wireless communication device with which the apparatus is allowed to perform a location finding operation, where the indication includes a first identity resolution key (IRK) unique to the wireless communication device; processing a payload including the announcement of the IRK; and comparing the IRK to a first IRK received by the always-on processor.

A method includes implementing, through a server of a Fifth Generation (5G) mobile network, a pseudo-Authentication Server Function (AUSF) component as a component of an architecture of a core mobile network of the 5G mobile network distinct from an existing AUSF module thereof executing on the server, and automatically routing a request for identity resolution of a User Equipment (UE) connectable to the 5G mobile network to the pseudo-AUSF component instead of the existing AUSF module, with the request including a concealed identifier of the UE. The method also includes automatically resolving the concealed identifier into a permanent identifier of the UE utilizing an existing Unified Data Management (UDM) module of the core mobile network based on communication between the pseudo-AUSF component and the existing UDM module, and automatically providing, through the existing UDM module and the pseudo-AUSF component, the permanent identifier of the UE to address the request.

A method of resolving an identity of an entity is disclosed. Entity data is ingested. Identity fragments observed in the entity data are recorded. All or a subset of identifiers that are connected across the identity fragments are discovered. A directed graph of the identity fragments is built. An entity profile is produced by applying a set of rules to the directed graph.

An electronic device is provided. The electronic device includes communication circuitry, at least one processor including processing circuitry, and memory including one or more storage mediums, storing instructions, wherein the instructions that, when executed by the at least one processor individually or collectively, cause the electronic device to receive, via the communication circuitry, an advertising packet advertised from an external electronic device, determine, based on a first list of identity resolving keys (IRKs) of other electronic devices whether a resolvable private address (RPA) of the external electronic device in the advertising packet is resolved, and based on determining that the RPA of the external electronic device is not resolved, request, from the external electronic device, via the communication circuitry, a version of a second list of IRKs.

A system and method for privacy-preserving identity resolution using deep learning enables accurate matching of personally identifiable information (PH) while maintaining data security. The system employs a deep learning model trained with transformer architecture and contrastive learning on third-party identity graph data. Custom tokenizers process data by leveraging hierarchical structures and domain-specific characteristics. The trained model generates vector embeddings that enable fuzzy matching, accounting for variations in spellings, typographical errors, and data inconsistencies. A vector database stores embeddings for nearest neighbor searches to identify potential identity matches. The system enables identity resolution without requiring Pll data movement from first-party environments. The invention facilitates building accurate first-party identity graphs and enables secure collaboration between parties without exposing underlying Pll data.

A computer-implemented method for maintaining unified guest identity records across heterogeneous hospitality data systems. The method receives guest interaction data from independent data sources operating on different schemas, normalizes identifiers through standardization algorithms, and applies two-stage matching comprising deterministic cryptographic hashing followed by probabilistic Bayesian inference. Core attributes are stored in a relational database with strong consistency guarantees while behavioral data is maintained in a document database with eventual consistency, synchronized through a change data capture mechanism providing near real-time synchronization. The identity resolution engine operates continuously in real-time, reducing storage requirements compared to systems allowing duplicate records. The system further implements a predictive segmentation model using online learning to adjust parameters in real-time while preserving accuracy for previously learned patterns. An event-driven workflow orchestration architecture records state-changing guest actions as immutable events and dispatches them to independent CRM, marketing automation, event management, and analytics modules that autonomously respond without centralized orchestration, enabling high-throughput processing with low latency.

The present application discloses an industrial equipment management method, equipment, and storage medium based on identity resolution. The method includes: encoding industrial equipment based on preset encoding rules to determine the equipment identification code corresponding to each industrial equipment; determining a number of industrial equipment corresponding to an identity resolution terminal and a data acquisition module corresponding to each industrial equipment, and initializing the identity resolution terminal; receiving industrial data sent by the data acquisition module based on the identity resolution terminal, and preprocessing the industrial data to determine the standard data corresponding to the industrial data; inputting the standard data into a preset data model template to determine the corresponding data analysis model for the industrial equipment; inputting the data analysis model into a preset equipment management solution generation algorithm, analyzing the industrial equipment, and determining the equipment management solution for the corresponding industrial equipment. The present application realizes the management of industrial equipment by utilizing the industrial data corresponding to the industrial equipment through the above method.

Provided are a method and system for identity correlation between a transaction applicant (TA) and a watchlist entity (WE). Preexisting watchlist data and other aggregated identity data (AID) are processed to provide for comparison to a collective identity of at least the TA. Using various categorizations for the AID and the collective identity, watchlist tags are generated that can then be matched to the collective identity. As a result of the matching, a watchlist candidacy demonstrating a probability that the identity of the TA does or does not correspond to that of the WE can be generated.