14278 results about "Access control" patented technology

A system and method for implementing a convergent intelligence fabric (CIF) for distributed artificial intelligence operations. The CIF architecture integrates tensor-theoretic foundations, probabilistic cache management, precision-aware memory operations, quantum-resistant security, and neural-based optimization within a unified framework. The system orchestrates asynchronous, multi-hop data flow among computational resources while maintaining data security through per-block encryption and identity-based access control. Key components include a universal multi-model KV cache subsystem, agent-parallel disaggregation pipelines, reinforcement learning-based orchestration, and neuromorphic memory integration. Advanced implementations incorporate graphon-enhanced memory for sparse graph sequences, multi-modal cognitive persistent memory, and quantum-resistant asynchronous multi-domain trust protocols. The system enables efficient cross-agent collaboration, sophisticated knowledge sharing, and secure cross-domain operations while optimizing computational resources and maintaining strict privacy guarantees across distributed AI deployments.

The present disclosure relates to a blockchain-based authentication system for electric vehicles and drones, and a method for authenticating electric vehicles or drones in a smart city. The proposed blockchain-based authentication system enhances security, privacy, and scalability for electric vehicles (EVs) and drones in smart city environments. It employs a consortium blockchain managed by city authorities and stakeholders, utilizing smart contracts for identity registration, credential issuance, and access control. Device nodes store cryptographic keys securely, while an optional authentication server facilitates off-chain integration. The authentication operation of the system includes, ensuring tamper-proof identity verification through on-chain validation. Authentication attempts are immutably recorded for auditing and anomaly detection. By eliminating single points of failure and strengthening data privacy, this decentralized authentication framework offers a scalable and secure solution for integrating EVs and drones into smart city infrastructures.

The invention relates to the technical field of privacy data protection, and discloses a privacy data protection method and system based on homomorphic encryption and federated learning, and the method comprises the steps: processing original health data collected by Internet of Things equipment of a plurality of pension institutions, and obtaining a preprocessing data set and a privacy risk assessment result; encrypting to obtain a double encrypted data set; constructing a decentralized federated learning framework, and calculating global training parameters; performing noise injection processing and model iteration training to obtain a privacy protection model; performing trusted execution environment deployment on the privacy protection model, and performing identity verification through role-based multi-level access control and zero-knowledge proof to obtain a security access strategy; edge node distributed deployment is conducted on the privacy protection model, cross-mechanism health data analysis is conducted through a secure multi-party computing protocol, a collaborative analysis result is obtained, various privacy protection technologies are adopted, and efficient utilization of data is achieved while data privacy is protected.

The invention provides a cross-domain network security policy automatic generation and protection policy collaboration method and system, and relates to the technical field of network security, and the method comprises the steps: collecting multi-domain security event data; constructing a security threat map; generating a cross-domain security risk score by using a deep learning algorithm; determining an inter-domain access control rule set and an intra-domain protection strategy according to the score; forming a cross-domain collaborative protection strategy; deploying a strategy on the multi-domain security device; establishing an inter-domain information sharing channel; monitoring the strategy effect; optimizing the strategy by using a reinforcement learning algorithm; and updating to each domain security device. According to the method, the effectiveness of cross-domain network security protection is improved.

The present invention provides a facial recognition system using deep learning methodologies while integrating privacy-preserving capabilities. This system employs convolutional neural networks (CNNs) to extract and classify facial features, ensuring high accuracy in recognition tasks. Moreover, the system addresses privacy concerns by incorporating techniques such as facial feature encryption and anonymization, thereby enhancing user privacy and data security. This invention is applicable across various domains, including security, surveillance, access control, and personalized services, where facial recognition is utilized while preserving individual privacy.

The invention provides a private domain live broadcast data storage and visitor authentication method and system based on a block chain, and relates to the technical field of data storage and access control, and the method comprises the steps: collecting multi-mode biological characteristic data of a visitor, carrying out the feature extraction and fusion through a deep neural network of a space-time attention mechanism, and carrying out the authentication of the visitor; authenticity verification is carried out in combination with an adversarial generative network, a dynamic identity token is generated, and a hash value of the dynamic identity token is written into the block chain. The method comprises the following steps: performing semantic analysis on live broadcast data flow through a self-adaptive content perception model, constructing a double-layer access control matrix according to a data sensitivity level and a credibility score, performing self-adaptive fragmentation and hierarchical encryption based on federated learning, and then storing. After an access request is subjected to identity verification, an optimal edge node is selected according to access authority and network performance parameters to construct multi-path transmission, and a transmission strategy is generated based on a service quality score and a data sensitivity level. And when the service quality is lower than a threshold value, triggering the smart contract to perform fault migration.

Network infrastructure for user-specific generative intelligence. Providing user-specific context to a generically trained LLM introduces a variety of complications (privacy, resource utilization, training costs, etc.). Various aspects of the present disclosure provide novel user-specific data structures, privacy and access control, layers of data, and session management, within a network infrastructure for generative intelligence. For example, user-specific embedding vectors may be used to provide user context to a generically trained foundation model. In some variants, edge devices capture multiple modalities of user context (images, audio; not just text). Privacy and access control mechanisms also allow a user to control information that is captured and sent to the foundation model. Session management further decouples a user's conversational state from the foundation model's session state. These concepts and others may be used to emulate e.g., a chatbot based virtual assistant that responds based on user context.

The invention provides an unmanned aerial vehicle multi-system navigation decoy system based on intelligent algorithm optimization, and relates to the technical field of unmanned aerial vehicle processing, a sensing module comprises a cognitive radar, a photoelectric / infrared sensor, a magnetic and acoustic sensor and an electromagnetic environment scanning device, and a digital twinning and countermeasure AI module comprises a digital twinning model module and a countermeasure AI engine; the cross-domain multi-source cheating module comprises a multi-system navigation signal synthesis module and a sensor-level cheating module; the reconfigurable intelligent antenna array comprises a metamaterial unit, a real-time beam controller and a biological heuristic intelligent algorithm module, and the system security hiding module comprises an encryption access control unit, a micro-power frequency expansion and hopping unit and a redundant backup and disaster recovery unit. The response time of the system from detection to interference can be remarkably shortened by performing high-fidelity simulation on the flight control logic of the unmanned aerial vehicle and the actual electromagnetic environment in the digital twin environment and combining the hybrid decoy strategy generated by the adversarial AI.

Systems and methods for data streaming with attribute-based access control. A client apparatus in a data streaming system receives a message via a data stream, form a data stream provider. The message has a schema version of the schema associated therewith. The client apparatus processes the message to retrieve a record, the record including a data item and a data tag corresponding to the data item. The client apparatus, with reference to the schema version, determine whether an identifier (e.g., client ID) of with the client apparatus has permission to access the data item associated with the data tag. After detecting that the identifier does not have permission to access data associated with the data tag, the client apparatus masks the data item to produce a masked data record. The client apparatus releases the masked data record for further processing by the client apparatus.

The invention provides a block chain-based distributed device identity authentication and access control method and system, and relates to the technical field of block chains, and the method comprises the steps: generating a target device fingerprint by a to-be-accessed device based on a physical cloning-free function of the to-be-accessed device, binding the target device fingerprint with a device public key to generate device identity information, and sending the device identity information to the to-be-accessed device; carrying out block chain evidence storage on the equipment identity information through an identity authentication smart contract; the authentication node queries corresponding equipment identity information based on an equipment fingerprint sent by the to-be-accessed equipment, and verifies the digital signature in the access authentication request by using the equipment public key; the access device which passes the authentication and obtains the encrypted session key obtains an access control strategy pre-configured by an administrator through an access control smart contract; and when needing to access network resources, the access equipment accesses the network resources based on the access control strategy.

A driver monitoring system includes internal and external cameras attached to a vehicle. The cameras capture video of the vehicle's driver and the area surrounding the vehicle for later review by a coach or supervisor. To ensure privacy of people who appear in video, portions of the video may be processed to blur faces, license plates, and / or other features. Furthermore, access control mechanisms exist so that only users in specific roles have access to review certain types of video.

Systems and methods are directed to incorporating large language model (LLM) prompts into graph queries using graph query language and executing these graph queries. The system receives a graph query that includes a LLM prompt in the graph query language. The LLM prompt comprises an input context and a query to be answered. Based on the graph query, the system traverses a knowledge graph to obtain context data associated with the input context. The context data may be constrained by access control policies of an enterprise providing the knowledge graph. A context table may be generated using the context data based on the input context indicated in the LLM prompt. The system then invokes an LLM to determine a result by applying the query of the LLM prompt to the context data obtained from the traversal of the knowledge graph. The result is then presented on a user interface of a client device.

Apparatuses, systems, and methods for managing access to metadata stored at a memory. To manage access to metadata, a mode register is configured to receive a metadata enable setting and to provide a metadata enable signal based on the metadata enable setting. A metadata access control circuit configured to receive a column address identifying a particular column to be accessed within a memory array. The metadata access control circuit blocks access to a column corresponding to the column address when the column address matches one of a plurality of particular column addresses designated for storage of metadata and the metadata enable signal has a first value, and permits access to a column corresponding to the column address when the column address is different than every one of the a plurality of particular column addresses designated for storage of metadata or the metadata enable signal has a second value.

The invention discloses a medical image encryption and secure storage method and system based on a block chain, and the method comprises the steps: obtaining the original data of a medical image, segmenting the original data of the medical image into a plurality of segments through a fragmentation technology, carrying out the encryption of each segment through a symmetric encryption algorithm, generating an encrypted segment set, and storing the encrypted segment set in a database; determining an integrity check value of the encrypted fragment set; according to the encrypted fragment set, adopting a distributed storage protocol to distribute each fragment to a plurality of storage nodes, recording a storage position and an integrity check value of each fragment to a block chain account book, and generating a storage index containing a storage path and check information; and establishing an access control strategy through the block chain smart contract, and if the obtained requester identity identifier and the authority level conform to the access control strategy, generating a dynamic authorization token including an access range and time limitation. According to the method, the privacy of the patient is protected, and meanwhile, the safety and availability of medical data are improved.

Network infrastructure for user-specific generative intelligence. Providing user-specific context to a generically trained LLM introduces a variety of complications (privacy, resource utilization, training costs, etc.). Various aspects of the present disclosure provide novel user-specific data structures, privacy and access control, layers of data, and session management, within a network infrastructure for generative intelligence. For example, user-specific embedding vectors may be used to provide user context to a generically trained foundation model. In some variants, edge devices capture multiple modalities of user context (images, audio; not just text). Privacy and access control mechanisms also allow a user to control information that is captured and sent to the foundation model. Session management further decouples a user's conversational state from the foundation model's session state. These concepts and others may be used to emulate e.g., a chatbot based virtual assistant that responds based on user context.

The invention relates to methods and systems for integrating generative artificial intelligence (AI) capabilities into Software as a Service (SaaS) platforms. It comprises maintaining AI agents with varying credentials, enabling their interaction with alphanumeric data in table structures, and implementing a hierarchical access control scheme. The system displays table structures, provides interfaces for user inputs, and allows AI agents to be added as platform users. The generative AI agents can analyze data, identify actions, and perform tasks autonomously. The invention also includes methods for proactive information gathering, interactive analysis of AI outputs, and management of AI resources as limited assets. This approach enhances SaaS functionality by enabling AI-driven task completion, data analysis, and decision-making while maintaining data security and user-specific access controls.

The invention discloses a cloud desktop security access control method based on a zero-trust architecture, belongs to the technical field of network security, and is used for solving the problems of difficulty in hidden attack detection, cross-cloud attack chain breakage and conflict between security control and service continuity in a multi-cloud environment. Firstly, user identity attributes, session metadata and service call logs are aggregated, an identity-resource-behavior triple dynamic graph is constructed, cross-session association features are extracted, and a multi-dimensional behavior baseline is generated. And quantifying the access deviation degree based on the behavior baseline, triggering sensitive operation traceability analysis, constructing a time sequence risk propagation model, identifying latent attack features, predicting a penetration path and outputting a risk propagation coefficient. And finally, dynamically generating a process-level micro-isolation strategy according to a risk result, gradually adjusting the authority through a nonlinear authority attenuation function, inserting a secondary authentication node when unexpected resource jump is detected, reconstructing a communication white list, and realizing collaborative optimization of security protection and service continuity.

The invention discloses a human resource management method and system based on data security. The method comprises the steps that multi-source human resource data are collected through a standardized interface, and sensitive levels are marked in a classified mode; calling a dynamic encryption engine based on the sensitive level and the data type, performing asymmetric encryption on the structured data, and performing hybrid encryption on the unstructured data; a dynamic hierarchical access control strategy is generated in combination with user roles and service scenes, and the permission range is adjusted in real time; laws and regulations such as GDPR and CCPA are analyzed through a compliance rule base, data operation legality is automatically verified, and illegal behaviors are blocked; k-anonymity and differential privacy technologies are adopted to desensitize sensitive data, and privacy protection and data availability balance are ensured; and recording a full-process operation log based on the block chain and generating a non-tampering audit report. The system comprises a data classification acquisition module, a dynamic encryption engine module, an access control engine module and the like. According to the method, the problems of data protection rigidness, compliance response lag and privacy-utility imbalance of a traditional HR system are solved.

The invention discloses a distributed multi-tenant data security isolation system and method, and relates to the technical field of data isolation, and the system comprises a data isolation module which is responsible for distributing independent container instances and hardware resources for each tenant, and adjusting the data access authority according to an access control strategy; the dynamic access control module is responsible for generating an access control strategy according to the user identity, the authority, the tenant strategy and the access behavior; the distributed data management module is responsible for storing data fragments on different storage nodes; the resource scheduling and optimizing module is responsible for collecting resource demand data of each tenant, calling an intelligent scheduling algorithm, performing dynamic prediction and analysis on resource demands, and then automatically adjusting a resource allocation strategy according to a prediction result; and the security auditing and monitoring module is responsible for collecting operation logs and event records of each module and carrying out comprehensive analysis. The invention aims to solve the problems of data security and resource management in a multi-tenant environment.

The invention provides a service data security protection method and system for digital enterprise management, and the method comprises the steps: obtaining an access request sequence initiated by a target user at a service operation terminal, generating a dynamic access control strategy vector according to a historical behavior track associated with an access operation identifier, and carrying out the dynamic access control strategy vector; analyzing the dynamic access control strategy vector through a strategy decoder, and generating a real-time access permission instruction; based on the real-time access permission instruction, performing homomorphic encryption mapping on a sensitive data segment in the request context information to generate a ciphertext transmission channel, and performing security parameter synchronization on the ciphertext transmission channel and the cross-department conjoint analysis model; and calling a federated learning framework to carry out multi-modal feature fusion on the real-time operation flow of the service operation terminal, generating an abnormal operation confidence score, triggering a cross-level interception protocol when the abnormal operation confidence score exceeds a dynamic threshold, and rolling back the current session state to a security baseline version. According to the invention, the accuracy and response timeliness of anomaly detection can be improved.

The invention discloses a data security and privacy management method and system based on a block chain technology, and the method comprises the following steps: S1, collecting original data in a target system, and carrying out the preprocessing of the original data; s2, constructing an access control strategy set; s3, converting into a structured strategy configuration file, and deploying a smart contract; s4, matching and judging the access request and a preset strategy by the intelligent contract; s5, judging whether the access request is legal or not; s6, performing structured processing on the access log, and writing the access log into a block chain account book; s7, intelligently selecting an encryption mechanism, and providing access return data to the access party; and S8, carrying out modeling prediction on the access mode by adopting a federal gradient lifting tree algorithm. The block chain intelligent contract and the federal gradient boosting tree algorithm are fused, differential privacy, zero-knowledge proof and homomorphic encryption technologies are combined, data intelligence and refined privacy protection are realized, and the method has the advantages of high credibility, high intelligence and high traceability.

The invention provides a security management and retrieval method based on Internet of Vehicles data, and the method comprises the steps: carrying out the cooperative training of a global key management model through a federated learning technology on the premise of not leaking the original data of all parties, and achieving a security key management scheme under the participation of multiple parties through a model parameter aggregation and updating mechanism; when data retrieval is needed, a user submits a retrieval request and identity authentication information, and after a system verifies the identity of the user, a ciphertext data identifier meeting conditions is matched in an attribute index according to a keyword and an authorization attribute of the request; the system acquires corresponding ciphertext data from a distributed database according to the ciphertext data identifier, and directly performs keyword matching calculation on the ciphertext data through a secure multi-party computing protocol under the condition of not decrypting the data; and according to the mined security threats and abnormal modes, a data encryption strategy and an access control strategy are dynamically adjusted, authority limitation or access blocking is performed on suspicious users, and the security protection capability of the system is improved.

The invention relates to the technical field of integration of artificial intelligence security and network security, and discloses an AI dynamic security transmission system based on an SASE framework, which realizes security access control based on AI dynamic identity verification through an SASE integration access module, acquires and predicts network performance change in real time by using a network state sensing module, and transmits the network performance change to a network server. Equipment, environment and data content are subjected to multi-dimensional analysis by means of a security risk assessment module, a quantitative risk score is generated, and a transmission protocol, parameters and encryption strength are dynamically adjusted according to a network state and the risk score by means of a dynamic transmission optimization module and a self-adaptive encryption module; the problem that safety protection and transmission efficiency are difficult to cooperate in a traditional architecture is effectively solved, low-delay and high-reliability data transmission service can be provided for AI application in a complex network environment, meanwhile, self-adaptive dynamic protection of the whole data transmission process is achieved, and data safety is comprehensively guaranteed.

The invention provides an access control system based on face recognition and a recognition method, and the system comprises a multi-modal image collection subsystem which integrates a visible light camera, a depth camera and an infrared thermal imaging camera and is used for synchronously collecting a visible light image, a depth image and an infrared thermal imaging image of a face; the image preprocessing unit is used for carrying out corresponding preprocessing on the image; the feature extraction network module is used for extracting image features; the multi-modal feature fusion and alignment module is used for calculating a mutual information matrix by adopting a feature fusion strategy based on mutual information maximization and adaptive weighting, determining an adaptive weight and performing weighted summation, and performing feature alignment by applying a method based on canonical correlation analysis; and the face recognition classification and verification module verifies the image by adopting a multi-level fusion strategy. According to the method, the false recognition rate and the rejection rate are effectively reduced, the strong anti-cheating capability is achieved, and cheating attacks of various fake face means can be accurately recognized and resisted.

The invention discloses a smart park safety protection system and method based on smart video analysis and multi-mode integration, and relates to the technical field of smart park management. The system comprises a camera, an environment microphone and an access control terminal which are deployed in a park, an edge computing node is used for extracting structured video, audio and access control features, a central server realizes multi-modal space-time alignment through a self-calibration module, a multi-modal fusion module constructs a joint representation vector based on a low-rank tensor algorithm, and the joint representation vector is used for realizing multi-modal space-time alignment. And the security decision module identifies an abnormal event and triggers a response strategy in combination with a classification and anomaly detection model. According to the invention, multi-mode cooperative identification, high-robustness behavior analysis and intelligent response linkage are realized, and the real-time performance, accuracy and intelligent level of a park security system are improved.

The invention relates to a power edge cloud cooperative management method based on a swan OS, and the method comprises the following steps: S1, deploying intelligent edge nodes, setting an embedded AI acceleration unit, operating the swan OS on all the intelligent edge nodes, and achieving the seamless cooperation of equipment through the distributed characteristics of the swan OS; and S2, realizing self-organizing connection of edge devices by adopting a Mesh network technology, designing a direct communication path between the edge devices, and realizing rapid processing of local problems. S3, aiming at the real-time requirement of a power system, optimizing an MQTT / CoAP protocol stack, improving the efficiency and reliability of message transmission, and allocating special network resources for different types of power applications by utilizing the 5G network slice characteristics, S4, constructing a security architecture based on a zero-trust principle, enhancing the equipment access control and data transmission security, and S5, establishing a security architecture based on a zero-trust principle. And identity authentication and data integrity protection are performed by using a block chain technology, so that data tampering and counterfeiting are prevented. According to the method, the local distributed decision-making capability and the global cooperation capability are effectively improved.

Provided are a method and device for controlling a cell change operation of a mobile communication terminal The method may include receiving an upper layer message including one or more pieces of candidate target cell configuration information for an L1 / L2-triggered mobility (LTM) operation; receiving a medium access control control element (MAC CE) instructing the execution of the LTM operation on a target cell; and performing a detach operation from a source cell on the basis of the MAC CE, and performing the cell change operation by applying the candidate target cell configuration information for the target cell.

The invention discloses a cloud-edge collaborative intelligent storage node dynamic deployment method and system. The method comprises the following steps: monitoring performance indexes such as edge node data traffic and storage resource state in real time; a deep learning algorithm combining time sequence analysis and an LSTM neural network is adopted to analyze traffic information, and a data access demand is predicted; edge nodes and cloud storage resource configuration are dynamically adjusted based on a prediction result, and an intelligent scheduling algorithm, a data cold and hot separation strategy and a self-adaptive fragmentation technology are introduced to allocate resources; the storage node layout is optimized in real time, and an optimal data distribution path is selected through a reinforcement learning strategy; data security and access control are realized by adopting end-to-end encryption, multi-level access control and block chain technologies. The system comprises a monitoring acquisition module, a prediction analysis module, a resource scheduling module, a path optimization module and a security control module. According to the method, the utilization efficiency of storage resources is improved, data delay is reduced, system stability and data security are enhanced, and the method is suitable for a cloud edge collaborative storage scene.

The invention provides an electronic certificate management and supply chain quality tracing method and system based on a block chain, and belongs to the technical field of information. According to the method, encryption storage and tamper-proof protection are carried out on the electronic certificate information through the distributed account book of the block chain and the Hash algorithm, and verification, approval and data updating operations are automatically executed based on the intelligent contract. Fine-grained authority management and abnormal access detection of all parties of the supply chain are realized through a user access control module; and through a tracing query module, performing multi-condition combination query on the electronic certificate information stored in the block chain, and generating a quality tracing report meeting supervision requirements. According to the method, through tamper-proofing, distributed storage and intelligent contracts of the block chain, trusted storage, automatic verification and full-chain quality tracing of the electronic certification are realized, data security and supply chain transparency are improved, trust cost is reduced, and supervision and tracing efficiency is improved.

The invention provides a security risk assessment method and system for digital assets, and the method comprises the steps: obtaining a unique identifier and an ownership record of the digital assets, and determining key points of risk assessment, the key points comprising encrypted storage, transaction history and a circulation mechanism of the digital assets; for each key point, identifying identity authentication, access control and data encryption security factors in the key point, and performing security compliance verification on privacy protection and compliance requirements of the digital assets by adopting a probability statistical method; generating a plurality of digital asset cross-chain compatibility and value evaluation factor combinations through a Monte Carlo simulation method, simulating security events and consequences thereof in different scenes, and calculating the occurrence probability and loss influence of each scene; and according to the optimized risk assessment model, assessing the overall security risk of the digital assets, identifying the key link with the highest risk, and assessing the potential loss influence of the key link.