905 results about "User privacy" patented technology

The invention relates to the technical field of power data security, and provides a cross-subject power data security sharing and collaborative analysis method fusing a block chain and privacy computing, and the method comprises the following steps: registering and authenticating the digital identity of each participant through the block chain; registering the hash abstract and the access control strategy of each main body data to a block chain through an intelligent contract; the analysis task initiator issues a collaborative analysis task on the chain; scheduling each data party to locally execute the privacy computing task according to the task content; the data security and privacy protection capability is strong: the data is not out of the domain, the original data is not transmitted, and the main body data security and the user privacy are ensured; a credible collaborative analysis mechanism: based on a blockchain contract mechanism and a data traceability function, the whole process can be verified and audited; the method supports a plurality of privacy computing modes, is compatible with a plurality of privacy protection computing frameworks such as federated learning, SMPC and TEE, and has good expansibility.

The invention discloses a cross-domain privacy protection method and system for advertisement recommendation, equipment and a medium, and the method specifically comprises the steps: carrying out the encryption matching of user behavior data and anonymized equipment data, and generating an initial cross-domain joint feature vector; fusing the initial cross-domain joint feature vector and the disturbance feature vector to form a target cross-domain joint feature vector; splitting a pre-trained advertisement recommendation model into a feature coding sub-module and a reasoning sub-module, deploying the feature coding sub-module to a user adjacent edge node, and retaining the reasoning sub-module in a user local device; and based on the target cross-domain joint feature vector, performing calculation of the feature coding sub-module and calculation of the reasoning sub-module, and uploading the encrypted hidden layer feature vector to a federated learning aggregation server for global model updating. According to the method, cross-domain data utilization and user privacy protection in an advertisement recommendation process are realized, and effective feature vectors are generated for personalized advertisement recommendation while data are guaranteed not to be out of a domain.

The invention discloses a dynamic portrait construction method and system fusing a large model and user behavior data, belongs to the technical field of artificial intelligence and big data analysis, and aims to solve the problems of insufficient real-time performance, difficulty in multi-source data integration and high privacy risk in the traditional technology. The method comprises the following steps: collecting basic attributes, behavior sequences and unstructured data by burying points, and processing through an Apache Flink session window and a dynamic watermark; multi-modal feature extraction (discrete feature embedding, bidirectional LSTM coding behavior sequence and BERT coding text) is carried out, and joint embedding is generated through cross-modal contrast learning; generating three types of labels, namely a static label (rule engine), a dynamic label (1.3 B parameter quantity Nano-vLLM) and a predictive label (XGBoost), and dynamically adjusting weights; and realizing global model updating through federated learning and differential privacy. The system comprises a data acquisition layer, a feature extraction layer and a label generation updating layer. The real-time performance and accuracy of the portrait are improved, the privacy of the user is protected, and the commercial value in e-commerce, finance and other scenes is remarkable.

The invention provides a credible data space construction method and system based on data sharing, and relates to the technical field of data processing.The method comprises the steps that S1, in response to a request of a user for sharing target data, credible preprocessing is conducted on the target data to obtain credible data; s2, establishing a decision-making capability model for a user to autonomously perform credible authorization on the credible data; s3, analyzing a multi-dimensional sharing risk of the trusted data; s4, planning an optimal authorization auxiliary unit sequence based on the multi-dimensional sharing risk and the decision ability model; s5, in the process that the user autonomously performs credible authorization on the credible data, sequentially executing the authorization auxiliary units in the optimal authorization auxiliary unit sequence; and S6, aggregating the credible data subjected to credible authorization to construct a credible data space. According to the method, the credibility and the security in the data sharing process are remarkably improved, and the quality of the shared data and the protection of user privacy are ensured.

The invention discloses an artificial intelligence home management system, which relates to the technical field of smart home and comprises a data acquisition module, a behavior analysis module, an environment sensing module, a cooperative control module and an energy consumption optimization module. According to the artificial intelligence home management system provided by the invention, through multi-source data fusion and cross-equipment collaborative decision, the intelligent management level of a home scene is remarkably improved, user behavior data can be deeply analyzed, a dynamic behavior model can be constructed, and a user habit mode can be accurately identified and predicted, so that the initiative and adaptability of equipment control are realized, and the user experience is improved. The system comprehensively senses environmental parameters, constructs an environmental state map, provides accurate environmental background information for a cooperative control strategy, ensures the accuracy of an equipment control strategy, combines equipment operation data with a user behavior scene, generates an energy consumption optimization scheme, ensures the comfort of a user, realizes high efficiency and energy conservation, and improves the user experience. And user privacy leakage is effectively prevented, and potential faults are early warned.

According to the video stream image optimization method and device provided by the embodiment of the invention, a dynamic fuzzy kernel generation mechanism is innovatively designed, and the data processing security is ensured through space-time decoupling and physical constraint. A content-motion double-branch network is constructed, and a reliable feature reconstruction system is established in combination with residual connection and space-time transformation. A physical constraint corrector is introduced, and high-quality video output is provided while user privacy is protected through adaptive fusion and rule correction. According to the method, the defects of the traditional technology in the aspects of fuzzy processing, feature decoupling, image optimization and the like are effectively overcome.

The invention relates to a charging pile multi-mode vehicle security monitoring method and system, and the method comprises the steps: collecting vehicle contour point cloud, environment parameters and voltage fluctuation data through a stereoscopic vision sensor, a temperature and humidity sensor and a current sensor, and generating a multi-mode fusion data set through time-space synchronization; comprehensive scores of vehicle deformation, environment sudden change and voltage abnormity are calculated based on a dynamic weight fusion model, low-risk, medium-risk and high-risk three-level safety judgment results are generated in combination with a quantile threshold interval, and the environment adaptability defect of a traditional fixed rule is eliminated through a dynamic weight and an adaptive threshold; triggering a hierarchical control instruction according to a judgment result, and synchronously constructing a credible evidence chain containing a timestamp, an operation log and an encryption check code to ensure that the security event is traceable and the data is tamper-proof; and finally, aggregating multi-node user feedback through a federated learning framework, updating a model weight and a threshold interval in combination with a differential privacy technology, and realizing dynamic optimization of a false alarm rate while protecting user privacy.

The invention discloses an intelligent terminal multi-mode sentiment analysis method and device and a server, and belongs to the field of computers. The method comprises the following steps: on an intelligent terminal side, acquiring multi-modal data such as texts, voices and physiological signals and extracting features; through intra-modal adaptive attention and cross-modal bidirectional attention calculation, a fusion feature vector is generated; the emotion category and intensity are determined based on the vector, and local updates including model parameter updates and knowledge sub-graphs are generated. The local updates are sent to a federated aggregation server that constructs a global knowledge graph based on updates received from a plurality of terminals and generates a global model. The terminal receives the global model to update the local model. The invention aims to solve the problems of privacy leakage risk, inaccurate multi-modal fusion and poor model adaptability in sentiment analysis in the prior art, and achieves the technical effects of remarkably improving sentiment analysis accuracy and personalized adaptability while protecting user privacy.

The invention discloses a method for predicting an abnormal behavior state of an elderly living at home based on digital twinning, and the method comprises the steps: obtaining a social relation network data flow and a non-intrusive energy consumption data flow of the elderly living at home, and constructing a time sequence dynamic social relation graph based on the social relation network data flow, meanwhile, a semantic activity event chain is generated based on energy consumption data flow overload decomposition, cross-modal fusion processing is carried out on the social relation graph and the activity event chain, a composite state vector is generated, and based on a time sequence of the composite state vector, the social relation graph and the activity event chain are subjected to cross-modal fusion processing; and predicting the probability of entering a preset abnormal behavior state (such as social isolation, depression tendency or decline of self-care ability) of the home elderly. According to the invention, the privacy of the user is protected through non-intrusive data acquisition, and the social communication and exclusive activity information of the elderly are fused, so that the transformation from post-event alarm to pre-event early warning is realized, a precious time window is won for guardians or service institutions to take preventive intervention measures, and remarkable social and humanistic values are achieved.

The invention discloses a privacy protection recommendation method based on graph federal learning, and belongs to the technical field of intelligent recommendation. The method mainly comprises the following steps: a client receives an initial weight, and constructs an initial user-article interaction graph based on user local data; modeling interaction between nodes on the initial user-article interaction graph based on the graph neural network and the initial weight; training the local user-article interaction model based on user local data; the server side aggregates the article embedding matrix gradient uploaded by each client side; the server side clusters the updated article embedding matrix to generate a sampling article set; and the client performs multi-task joint training based on self-supervised learning on the local user-article interaction model based on the user local interaction subgraph, and performs article recommendation based on the trained local user-article interaction model. The problem of unbalanced data distribution can be relieved, and meanwhile the risk of user privacy disclosure is reduced.

The invention discloses a voice privacy leakage detection and desensitization system and method based on an end-to-end model, and belongs to the field of voice recognition and privacy protection. The problems that semantic desensitization is not thorough, the voiceprint protection effect is poor, and full-process auditing is lacked are solved. According to the technical principle part, a voice recognition module, a semantic desensitization module, a voiceprint desensitization module and a log auditing module are included, original audio is input, and Chinese voice transcription is completed through a pruned and optimized Whisper-medium model; and the semantic desensitization module and the voiceprint desensitization module regenerate a new audio, the voiceprint similarity is ensured to be less than or equal to 0.3 through verification, and the identity traceability of the speaker is cut off. Log auditing records the processing process in real time, and abnormal behavior early warning is conducted. According to the method, a multi-level privacy leakage detection defense line is formed, abnormal behaviors can be found in time, and user privacy safety is guaranteed.

The invention discloses a data protection method and system based on federated learning and homomorphic encryption, belongs to the technical field of data security, and aims to solve the technical problem of how to effectively protect user privacy while ensuring data availability. Comprising the following steps: a medical data node performs model training on a local AI model deployed locally based on a local data set, and uploads an encryption model gradient to a federal coordination server; the global parameters issued by the federal coordination server are decrypted, and gradient updating is performed on the local AI model based on the decryption result of the global parameters; calling the trained local AI model to perform model reasoning on the basis of the reasoning request for the third-party user who passes the identity authentication and has the access permission, and returning a reasoning result to the third-party user; and based on the auditing log, analyzing the running state and resource consumption of the medical data node according to a predefined auditing rule, and generating alarm information based on the abnormal operation.

Systems and methods for determining user intent and providing targeted advertisements using chat robot interactions are disclosed. The system receives user cues during a chat session with the chat robot and generates a response using a large language model. User intent is extracted by analyzing chat conversations using natural language processing and machine learning techniques. The extracted user intent including weighted keywords and concepts is used to create a user intent profile. Targeted advertising content is generated based on the user intent profile, and the targeted advertising content is provided to the user during subsequent platform interactions. The large language model is continuously retrained using the user participation data to improve intent modeling accuracy. User privacy is maintained by limiting context extraction to chat robot conversations. The system enables personalized and related advertising by inferring user intent via dialog interactions.

An electronic device may determine, based on a permission access record of an application, whether a privacy risk exists. If the privacy risk exists, the electronic device may remind and guide a user to perform permission optimization, which reduces a risk of user privacy disclosure and improves user experience.

The invention discloses an identity authentication method and system, an electronic device, a storage medium and a program product, and relates to the technical field of security. Only when a legal user is determined for the first time, a key distribution center knows a real name corresponding to a client, a subsequent signature, a bill authorization bill generation process and a subsequent anonymous access process to a corresponding server; in the embodiment of the invention, the clients initiate with the artifacts, so that the key distribution center and the corresponding server do not know the association relationship between the artifacts and the clients, and the privacy of the user is guaranteed.

The invention relates to an Internet of Things equipment identification method based on equipment behavior analysis. The method comprises the following steps: extracting periodic communication features and information entropy features of Internet of Things equipment on distributed nodes; the optimal feature subset is selected through chi-square verification, and the recognition accuracy is further improved; capturing time dependence and dynamics of behavior modeling by using an LSTM network, and aggregating local behavior patterns of each node through federated learning to form a global model; training and evaluating the model to obtain an optimal model and outputting a recognition result; according to the method, original data are not shared, only model updating is shared, user privacy is effectively protected, and compared with the prior art, the method has remarkable advantages in the aspects of privacy protection, recognition accuracy, expandability and robustness, is suitable for a complex and changeable Internet of Things environment, and has wide application prospects. The method can be widely applied to the fields of intelligent home, intelligent wearing, intelligent transportation and the like, and has important practical significance and wide application prospects.

The invention discloses a new energy vehicle supply chain data protection method, system and product based on a block chain and zero knowledge proof, and the method comprises the steps: constructing a decentralized, tampering-free and auditable supply chain data sharing platform through a block chain technology; by adopting an intelligent contract, automatic identity verification, production record evidence storage, order scheduling and reputation management are realized; a zero-knowledge proof technology is applied, and compliance verification and fund flow privacy protection are completed on the premise that sensitive information is not leaked; through a dynamic reputation point mechanism, an order allocation strategy is optimized, and the overall resource utilization rate and cooperation efficiency of the supply chain are improved; an encrypted bill system is adopted, and privacy protection and risk control in the supply chain finance process are achieved. The method pays attention to performance, safety, expandability and user privacy protection, and has good industrial application prospect and commercial popularization value.

The invention provides an anti-quantum identification signature method based on an on-lattice SIS problem. The method comprises the following steps: selecting system safety parameters and other related system parameters; generating a system public parameter and a master key by using the system security parameter and other related system parameters; generating a corresponding public and private key pair by using the user identity ID; the user generates a signature of the message by using the private key; and the verifier verifies the validity of the signature by using the system parameters and the user public key. According to the method, a lattice-based digital signature scheme is constructed by utilizing an SIS difficulty problem on a middle lattice, and quantum attack resistance security is provided for an information system for deploying the algorithm scheme; personal identity information of the user is used for registering and generating public and private keys, the key escrow problem participated by a third party is relieved, and the privacy security of the user is improved.

The embodiment of the invention provides an image processing method and device based on computer vision and artificial intelligence, and the method and device achieve the privacy protection of data through innovatively designing a multi-dimensional data safety collection mechanism and through feature branch isolation and space-time coding. A hierarchical feature fusion network is constructed, and a safe and reliable target tracking system is established in combination with environmental constraints and physical laws. A dynamic weight adaptive mechanism is introduced, and the accuracy of a processing result and user privacy are ensured through multi-scale feature extraction and prediction correction. According to the method, the data security is protected, and meanwhile, the defects of the traditional technology in the aspects of data fusion, feature extraction, prediction constraint and the like are effectively overcome.

The invention discloses an electronic ticket ordering and payment application system based on a block chain, relates to the technical field of block chains, and is used for ticket business whole-process digital management, and a user interaction module supports multi-terminal operation and information management; the block chain order management module is responsible for order chaining and state updating; the multi-mode payment module supports double-track payment and encrypted transmission; the comprehensive matching module is linked with the block chain order management module and comprises a meal subsystem, a tourism subsystem, a hotel reservation subsystem and the like, and the passenger ticket verification module realizes multi-mode verification; the data storage module stores key data; and the intelligent contract scheduling module automatically executes the pre-deployed contract. The problems that an existing system is poor in data safety, low in efficiency and insufficient in adaptability are solved, ticket transaction safety and process automation are improved, data synchronization is guaranteed, multiple languages and multiple currencies are supported, user privacy is protected, multiple scenes are adapted, and travel and supervision requirements are met.

The invention relates to the technical field of wireless communication network resource optimization, and discloses a wireless communication network data optimization method. The method comprises the following steps: performing spatial correlation analysis by acquiring base station positions and frequency spectrum data, and generating real-time base station grouping configuration; performing privacy protection processing on the local gradient data to generate an encryption privacy gradient; cross-base-station model aggregation is realized through secure multi-party calculation; constructing a space-time prediction model based on the graph neural network and the long-short-term memory network to generate a spectrum idle probability thermodynamic diagram; solving the spectrum allocation scheme by adopting a quantum optimization algorithm and generating an executable scheduling instruction; and finally, realizing closed-loop optimization through dynamic calibration. According to the method, on the premise of strictly protecting user privacy, the contradiction between privacy protection and improvement of the spectrum utilization rate in dynamic spectrum allocation is effectively solved through cross-base-station collaborative modeling of the spectrum demand characteristics of the space-time dimension, and the spectrum prediction precision and the resource allocation efficiency are remarkably improved.

The invention relates to the technical field of artificial intelligence and education technology cross fusion, and particularly discloses a new media course intelligent learning resource generation system based on generative AI, and the system obtains the multi-dimensional state data of a learner through the means of eye movement tracking, electroencephalogram collection, interaction behavior analysis and the like, and constructs a high-dimensional state space through fusion; differential induction logic programming and a depth feature extraction network are cooperatively compressed into a low-dimensional semantic meta-state space, and the model interpretability and decision-making efficiency are improved; dynamic matching of personalized resource types and difficulty levels is realized based on a graph neural network and reinforcement learning; calling a multi-modal generation model to generate texts, videos or interactive contents as required, and introducing a cross-modal consistency verification mechanism to guarantee the output quality; the system also has an online adaptive optimization capability, combines a symbol rule recombination mechanism and a differential privacy protection strategy, and takes account of user privacy security while ensuring long-term stable operation of the system.

The invention discloses a cross-platform advertisement joint modeling method driven by privacy calculation, and relates to the technical field of internet advertisements, and the method comprises the steps: carrying out the preliminary desensitization processing of original advertisement data through a pre-established privacy protection mechanism, employing a hierarchical encryption means for sensitive fields contained in the original advertisement data, and carrying out the desensitization processing of the original advertisement data; obtaining a desensitization data set processed in the first stage to obtain a data set of which sensitive information is preliminarily shielded, carrying out feature extraction on the data by adopting a generative adversarial network model according to the desensitization data set processed in the first stage, carrying out deep learning according to statistical characteristics of the data, obtaining an implicit distribution rule, and carrying out feature extraction on the data; determining a generated preliminary feature mapping result; according to the cross-platform advertisement joint modeling method driven by privacy calculation, privacy protection and multi-mode synthesis of advertisement data are realized, and the method can be widely applied to advertisement analysis and multi-scene application needing protection of user privacy.

The invention relates to an intelligent drawing management method and device and a storage medium, and is applied to the technical field of engineering drawing digital management, and the method comprises the steps: employing a CNN + Transform hybrid model, extracting the visual features of a drawing through a convolutional neural network, analyzing the semantic association in combination with a Transform architecture, achieving the three-stage classification of the drawing, improving the accuracy, and achieving the intelligent management of the drawing. A multi-modal retrieval engine is innovatively designed, text, sketch and three-dimensional space combination is supported to achieve real-time conflict detection, a dynamic detection algorithm based on operation log hash is adopted, operation sequence hash values are calculated through distributed nodes, millisecond-level conflict recognition is achieved, an increment rendering strategy is adopted, a designer can compare and modify differences in time, and the real-time conflict recognition is achieved. The conflict resolution efficiency is improved by 5 times; a double-chain block chain architecture is designed, an authority operation log is stored in a private chain to ensure efficient query, an authority change record is stored in a public chain to realize non-tampering, user privacy is protected, supervision and traceability requirements are met, and the calculation overhead is reduced by 30% compared with a traditional digital signature scheme.

This disclosure provides techniques to utilize decentralized identifiers (DIDs) and verifiable credentials for secure, privacy-preserving transactions. In one aspect, a method is provided that includes: receiving user information; determining a DID based on the information; providing the DID to a user device; verifying the user's identity by validating the DID and associated verifiable credentials; and performing a transaction based on the verified DID. Other aspects are provided, such as generating a public-private key pair for the user, associating the DID with the public key, and / or creating a DID document stored on a distributed ledger accessible to authorized entities. Further aspects include processing transactions through smart contracts on a blockchain network, which may involve converting central bank digital currency to fiat currency while maintaining user privacy, applying transaction limits based on verified identity attributes, and providing zero-knowledge proofs to auditors to verify compliance without accessing underlying transaction details.

The invention provides a snore stopping method and device, and the method comprises the steps: obtaining an original vibration signal of the neck of a user, and carrying out the preprocessing of the original vibration signal, and obtaining a target vibration signal; analyzing the breathing state of the user according to the target vibration signal to obtain a breathing state analysis result; judging whether intervention conditions are met or not according to the respiration state analysis result; when the intervention condition is met, the breathing state of the user is intervened according to the breathing state analysis result and a preset self-adaptive intervention rule. The breathing behavior related to the snoring behavior is detected based on the vibration signal, flexible intervention on the breathing state of the user is achieved in combination with the detection result and the self-adaptive intervention rule, sound collection is not needed, interference of snore of other users can be avoided, user privacy is not involved, meanwhile, smooth intervention experience can be provided for the user, and the user experience is improved. Therefore, the method can be widely applied to the technical field of smart home.

The invention provides a terminal authentication method and device, relates to the field of cloud desktops, and can improve the security of cloud desktop resource access. According to the specific technical scheme, information of a terminal is sent to a management platform through a client program of the terminal; verifying the information of the terminal through an authentication strategy of the management platform, wherein the authentication strategy comprises at least one configuration item; when all the configuration items of the authentication strategy are verified successfully, verifying the authentication information of the terminal; and when the verification of the authentication information of the terminal is successful, establishing a communication connection between the terminal and a cloud desktop. According to the invention, the client program can flexibly control the access of the terminal to the cloud desktop resource through the authentication strategy configured by the administrator on the management platform under the condition that the user does not perceive, so that the security is improved, the privacy and personal data of the user are guaranteed, the leakage risk is reduced, and the user experience is improved.

The embodiment of the invention provides a gene sequencing data analysis method and device, computing equipment and a readable storage medium, and belongs to the field of data processing. The gene sequencing data analysis method is applied to user equipment, and comprises the following steps: encoding gene sequencing data of a user to obtain a plurality of gene sequencing plaintexts corresponding to the gene sequencing data; encrypting each gene sequencing plaintext by using the public key to obtain a plurality of gene sequencing ciphertexts; sending the gene sequencing ciphertext to a server; receiving a gene sequencing data analysis result ciphertext sent by the server; and decrypting the gene sequencing data analysis result ciphertext by using the private key, and determining a gene sequencing data analysis result of the user. And the user equipment sends the plurality of encrypted gene sequencing ciphertexts to the server, so that the privacy leakage of the user is avoided. And the server performs data analysis by using the preset sparse model, and processes the to-be-detected gene sequencing ciphertext, so that the gene sequencing data analysis efficiency is improved.

The invention discloses an elevator localization interaction method and device, and belongs to elevator system design. The intelligent level and the user privacy protection capability of the elevator interaction system are improved. According to the method, voice signals of a user are collected in real time and are transferred to generate a structured text with a timestamp, confidence and a preliminary keyword; then, intention recognition is conducted on the structured text based on semantic analysis, and whether sensitive information of the elevator is involved or not is judged; for queries involving elevator sensitive information, the system retrieves relevant elevator information through a local knowledge base and generates a localized response, which is played through elevator sound equipment. In the process, the system is automatically disconnected from the Internet, and it is ensured that sensitive information of the elevator is not transmitted through the Internet. And for non-sensitive query, the system is allowed to access the Internet, and real-time data is acquired and returned to the user. The user privacy can be effectively protected, meanwhile, intelligent elevator interaction experience is provided, and the risk of data leakage is avoided.

The invention discloses a neural network model reasoning method for improving intermediate data security, and relates to the field of neural networks, in the method, in the neural network model reasoning process of an NPU, intermediate feature map data generated by each operator layer is encrypted in real time and stored in an end-side SoC memory in an encrypted form, and the security of the intermediate data is improved. Intermediate data leakage caused by analysis of an attacker on memory data can be effectively prevented, the intermediate data output by the neural network model is protected against cracking, threats in privacy leakage aspects such as member inference attacks can be effectively resisted, user privacy data is ensured not to be stolen, and user experience is improved. The data security of the neural network model in the reasoning process is greatly improved, and a powerful guarantee is provided for user information security.