1351 results about "User privacy" patented technology

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.

A computer-implemented method and system for privacy-preserving location verification in distributed networks comprises initializing a multi-modal biometric authentication system on a user device, generating cryptographic keys using a distributed key generation protocol, binding the cryptographic keys to biometric templates using a fuzzy vault scheme, constructing and broadcasting encrypted location beacons, and generating zero-knowledge proofs of location claims. The system includes user devices equipped with biometric sensors and verifier devices configured to validate location claims and maintain consensus in a blockchain network. The method implements real-time liveness detection for multiple biometric input types, executes fault-tolerant consensus algorithms with privacy preservation, and maintains a dual-scoring mechanism comprising device trust scores and user reputation scores. The system enables secure location verification while preserving user privacy through cryptographic protocols and biometric authentication in decentralized environments.

The invention discloses a privacy enhanced intelligent search method and system based on multi-round iteration. The method comprises the following steps: performing hierarchical semantic analysis on a query input by a user; splitting the complex query into sub-queries based on a task dependency graph algorithm; according to the sub-query, retrieving an evidence fragment from the multi-source data, constructing a semantic element coverage matrix to detect a knowledge gap, and if an uncovered element exists, generating a supplementary sub-query for iterative completion until a preset termination condition is met; integrating cross-modal data through a federated learning technology, and generating a structured knowledge graph fragment in combination with semantic vector alignment and an evidence fusion algorithm; performing dynamic desensitization processing on the retrieval result; and a closed-loop iterative updating mechanism is formed based on a user explicit and implicit feedback optimization retrieval strategy. The problems of traditional intelligent search in the aspects of semantic understanding depth, complex problem reasoning, search result accuracy and integrity, user privacy security and the like are effectively solved.

Systems, devices, apparatuses, and methods for more effectively protecting a user's private data and information regarding their browsing and other on-line activities. Embodiments remove constraints on an application developer by not requiring them to specify an authentication service provider as part of their application, and instead permit a user to make that choice in a protected and anonymous manner.

The invention relates to the technical field of AI interaction, and discloses an AI multi-modal emotion interaction memory terminal, which realizes microsecond-level synchronization of voice, facial expression and text data through a multi-thread acquisition engine, dynamically allocates each modal weight by adopting a multi-head cross attention mechanism, and adaptively adjusts modal importance based on a conversation context hidden state; when the cross-modal confidence difference exceeds a threshold value, a gating LSTM conflict resolution module is activated, and the multi-source data collaboration problem is solved; the emotional memory modeling constructs an emotional state transition topology based on a graph convolutional network, protects user privacy in combination with a differential privacy mechanism, and realizes associated event storage of millisecond backtracking of short-term memory and long-term memory. The technology integrates multi-modal dynamic perception, privacy security calculation and adaptive learning ability, significantly improves the real-time performance and personification degree of emotion interaction, and can be applied to the fields of intelligent customer service, emotion accompanying, health monitoring and the like.

The invention provides a personalized recommendation system and method for an intelligent terminal, and belongs to the technical field of artificial intelligence and big data, and the system comprises a five-element multi-mode dynamic perception module, a space-time attention feature fusion unit, a hierarchical federal transfer learning framework, a context perception enhanced recommendation engine and an edge-cloud co-evolution mechanism. The five-element multi-mode dynamic sensing module is used for synchronously collecting physiological features, environmental parameters, behavior data, spatio-temporal information and a social relation graph; and the space-time attention feature fusion unit is used for fusing the multi-modal data by adopting an ST-Transform model. According to the system, on the premise of ensuring the privacy of the user, the recommendation accuracy and real-time performance in a complex scene are remarkably improved, and a new technical normal form is provided for the personalized service of the intelligent terminal; according to the personalized recommendation method, cross-device transfer learning enables the time consumption of new user feature mapping to be greatly shortened, environment-driven brightness adjustment effectively reduces the visual fatigue of the user, and incremental learning obviously reduces the data volume updating demand of the model.

The invention provides a virtual human design and application platform, method and device based on artificial intelligence, and relates to the technical field of virtual digital humans. The method comprises the steps of performing local anonymization on multi-modal input data on user equipment, encoding generated anonymized multi-modal features to obtain a multi-modal feature vector, and inputting the multi-modal feature vector into an emotion calculation model to obtain a user emotion intensity quantized value; inputting the multi-modal feature vector into a context sensing model, and generating a user intention vector after context correction in combination with a knowledge graph; generating an updated personality parameter matrix according to the user emotion intensity quantized value and the user intention vector; and outputting voice waveform data, facial muscle motion parameters and skeleton joint coordinate data based on the personality parameter matrix, and driving the virtual digital human three-dimensional model to perform real-time rendering. According to the scheme, the naturalness, emotional resonance and long-term user retention rate of virtual digital human interaction can be improved, and user privacy data security is protected.

The invention discloses a financial deep counterfeiting real-time detection and defense method and system based on a multi-modal large model, and the method comprises the steps: obtaining multi-modal data in a financial transaction scene, and carrying out the desensitization of an edge end; performing dynamic time sequence alignment on the multi-modal data, and calculating a synchronous error of lip motion and voice by adopting a dynamic time warping algorithm; inputting the features into a dynamic risk modeling layer, and generating dynamic risk features in combination with the updated risk feature library; analyzing the features through a double-flow GAN detector, and outputting a forgery probability; and a detection result is input into a compliance verification layer, the supervision file is analyzed through a legal BERT, a structured rule is generated, and real-time transaction interception and block chain log recording are executed. The method protects user privacy and data security, combines the risk feature library updated in real time, and has high flexibility and adaptability. According to the design of the double-flow GAN detector, image and video stream information is fully utilized, and the accuracy and reliability of detection are further improved.

The invention proposes to generate a new MAC address for a non-AP station while being associated with an AP station, for use when associating again with the same AP station. For privacy reason, the same new MAC address is generated locally at both the non-AP station and the AP station using a shared function. This is done in a synchronized manned, preferably at the end of the association, e.g. when disassociating. The non-AP station disassociates with the AP station, and at any time in the future, it may associate again with the same AP station using this new MAC address. The AP station is therefore able to recognize the non-AP station, while outside observers only see a new MAC address and thus see a new non-AP station associating with the AP station. User privacy is therefore reinforced over time as the non-AP station associates multiple times to the same AP.

The invention provides an anonymization processing method and system for emotion data in a vehicle. The anonymization processing method comprises the steps of collecting multi-mode emotion data; correspondingly carrying out local preprocessing and multi-modal alignment processing on the multi-modal emotion data; corresponding sensitive emotion feature information in the preprocessed multi-modal emotion data is extracted through a lightweight recognition algorithm, and the sensitive emotion feature information at the recognized position is packaged in a unified mode; performing desensitization processing on various types of emotion modal data, and encapsulating and synchronizing desensitization results with labels; deep emotional feature extraction is performed on the image, the voice and the physiological signal through a multi-modal fusion model, and the extracted three types of deep features are fused to obtain an emotional representation vector; and inputting the emotion representation vector into a pre-trained emotion recognition model, and outputting the current emotion state of the passenger, including the specific emotion category and the corresponding confidence coefficient. According to the method, emotion analysis and transmission are performed after data anonymization is realized, and accurate judgment of the system on the emotion state is not influenced while privacy security of the user is ensured.

The invention discloses a trusted cloud security confidential privacy level product service method, and relates to the technical field of design of cloud computing security. The method comprises the following steps: acquiring a trusted cloud privacy security level and a hierarchical core security mechanism, establishing a binding relationship with a product, extracting technical features and performing quantitative scoring, and constructing a three-dimensional security matrix; obtaining a privacy grading service demand of the user, and establishing a demand matching model to screen a security preference grade product; obtaining different-dimension technical grade improvement times of the user security preference grade product, and carrying out synergistic effect analysis; if significant positive correlation exists, correlation analysis is carried out to obtain a dynamic correlation coefficient, a product optimization strategy is made, a three-dimensional security matrix is dynamically updated, and the trusted cloud security confidential privacy level product service system comprises a feature classification module, a product optimization module, a collaborative analysis module and an optimization updating module. According to the method, more accurate security product recommendation can be provided for the user, and the trusted cloud security service level is improved.

The invention discloses a privacy protection federated basic model continuous learning method in a multi-source new energy main body collaborative scheduling scene, and the method comprises a device, a system and a medium, and aims to solve the security defect of user power data transmission, reduce the communication overhead in the transmission process and improve the generalization performance of a federated learning model. Differential privacy protection is carried out on power data of a new energy main body power terminal device, and a homomorphic encryption technology is adopted, so that safe sharing of data and power dispatching optimization are completed under the condition that user privacy is not leaked.

Systems and processes are disclosed for a multidimensional, cross-node, hashchain, network-based Meta-Content Enabler (MCE) providing real time anomaly detection and impact analysis. Unique hexadecimal sequence identifiers are generated based on real time indexing and categorization to create a golden virtual metadata copy used by AI engine to determine content score to identify the degree of deviation therefrom, and to identify the hexadecimal nodes in the hashchain. The identified discrepancy is verified across cross-node hashchains to give end to end parallel impact analysis on the anomaly. By leveraging real-time monitoring, multidimensional verification, and blockchain-based storage, the system provides a robust and efficient solution for ensuring the accuracy and integrity of user activities. The system integrates privacy-preserving techniques, such as differential privacy or secure multi-party computation, to protect sensitive metadata. These techniques enable the system to analyze and process the metadata while preserving the privacy of individual users.

The invention provides a grouping federal recommendation method based on bilateral additive article embedding. A central server constructs a double-layer article embedding characterization structure under a federated learning framework: a client locally maintains user personalized embedding and an article local personalized embedding matrix, and a server generates global group shared article embedding through a dynamic clustering grouping mechanism; superposing global sharing embedding and local personalized embedding by adopting an additive fusion strategy to generate user side personalized article characterization; a progressive course learning scheme is designed, and smooth transition from complete personalization to additive representation is realized by dynamically adjusting a regularization weight coefficient; and a grouping and clustering process is optimized in combination with a knowledge migration strategy, and collaborative knowledge sharing across user groups is promoted. In a client local training stage, a personalized recommendation loss function based on binary cross entropy is constructed, global shared embedding and local embedding parameters are synchronously updated, and a server side updates a global model through grouping federation aggregation. On the premise of protecting user privacy, the problems that in traditional federated recommendation, article characterization is simplified, and personalized perception is insufficient are effectively solved, the accuracy of a recommendation system is remarkably improved, communication overhead is reduced, and the method is suitable for personalized recommendation services of privacy sensitive scenes such as e-commerce and content platforms.

The invention discloses an intelligent facial skin care monitoring management system which is composed of a hardware layer, a data layer, an algorithm layer and an application layer, the hardware layer collects facial skin images and external environment data through an RGB camera, a UV light sensor, a depth sensor and an environment sensor, and the data layer achieves encryption transmission and cloud storage of the collected data. The algorithm layer performs skin state detection, classification and trend prediction based on multi-modal data fusion, a deep convolutional neural network and a time sequence model, and the application layer provides skin care scheme suggestions and virtual trial functions through a personalized recommendation algorithm. The system can detect the multi-dimensional features of the skin at high precision, generate a dynamic skin care scheme, predict the change trend of the skin state, and improve the user experience through a visual interface and an interaction function. The method has the advantages of being high in detection precision, scientific in recommendation scheme, accurate in trend prediction, safe in user privacy and the like, and can be widely applied to skin care product research and development, personalized skin care management and beauty industries.

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 revocable attribute-based encryption method with strategy hiding, which is based on ciphertext strategy attribute encryption, solves the problem that user privacy is leaked due to disclosure of an access strategy, realizes revocation of fine-grained user attribute access authority, and is proved to be completely safe. In a system establishment stage, system parameters are disclosed, a public key and a master key are generated, an authoritative authority authorizes a data user, distributes a private key and generates an AGK tree and an attribute group key, a data owner generates a ciphertext according to the public key, an access structure set by the data owner and a selected secret value, and the ciphertext is transmitted to the data owner. And then hiding the mapping function by using a cuckoo filter, positioning the attribute by a data user through the cuckoo filter, updating a private key by using an attribute group key, and finally decrypting the ciphertext to obtain the wanted information.

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 relates to the field of household appliances, and provides an intelligent air conditioner control method, system and device based on federal learning and a medium. According to the environment data and the time information corresponding to the environment data, an air conditioner prediction operation mode is obtained through a global preference model; wherein the global preference model is obtained and issued by the aggregation server by utilizing federal learning on the basis of a local training model which participates in uploading of the client; according to the environment data, the time information corresponding to the environment data and the air conditioner prediction operation mode, an air flow distribution prediction result is obtained through an indoor simulation model created on the basis of computational fluid dynamics; and according to the airflow distribution prediction result, the flexible air guide mechanism is controlled to adjust the angle. The problems that a traditional air conditioner has the risk of user privacy leakage and is poor in multi-user adaptability are solved, and collaborative optimization of high efficiency, energy conservation, accurate airflow regulation and control and user privacy protection is achieved.

The invention belongs to the field of intelligent transportation, and provides a federated learning method and system based on bidirectional feedback knowledge distillation and differential privacy. Dynamic collaborative optimization of a client and a server is realized by designing a bidirectional feedback mechanism, and a knowledge distillation framework is designed at the client, so that the dynamic collaborative optimization of the client and the server is realized. Local model features in a non-independent identically distributed data environment are respectively extracted, traffic data privacy is protected in combination with an adaptive differential privacy algorithm, knowledge distillation and privacy protection strength is dynamically adjusted by means of a bidirectional feedback mechanism, the balance capability of model performance and privacy protection is remarkably improved, and the service life of a server is prolonged. According to the method, a self-adaptive knowledge distillation fusion strategy based on bidirectional feedback is designed, global model optimization is carried out, prediction precision and communication efficiency in a complex traffic scene are further improved, user privacy leakage is effectively avoided, and precision of a federal learning model and adaptability to the complex traffic scene are improved.

The invention discloses a privacy information desensitization method and system for a voice generation type large model, and relates to the technical field of artificial intelligence. Input voice data is discretized, Gaussian noise disturbance sensitive features are injected, and a cross-modal voice generation model is constructed in combination with three-stage training; and meanwhile, a cross-modal privacy enhancement mechanism is applied to detect and fuzzify sensitive information in real time in an output stage. According to the invention, the adaptability of a large-scale voice generation model in a privacy protection scene is improved, and comprehensive protection of user privacy is realized. And moreover, the capability of extracting user privacy information by an adversarial attacker is effectively limited, and the risk of sensitive information leakage in the data transmission, storage and generation process of the voice generation model is reduced. On the premise that privacy is ensured, the voice generation model can still keep high-quality generation performance, generated voice output has high naturalness and accuracy, and actual application requirements are met.

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.

This disclosure provides methods, devices, and systems for protecting user privacy while operating a personal computing device. The present implementations more specifically relate to privacy protection techniques for personal computing devices based on onlooker detection and classification. In some aspects, a computing device may include a display, one or more sensors, and a privacy controller that receives sensor data from the one or more sensors and controls the display based on a presence or classification of onlookers associated with the received sensor data. In some implementations, the privacy controller may compare any onlookers detected from the sensor data to a database of contacts associated with the user and classify each onlooker as a “trusted onlooker” or “nontrusted onlooker” based on the comparison. More specifically, the privacy controller may selectively activate a privacy protection mechanism associated with the display based on whether a nontrusted onlooker is detected.

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 relates to the technical field of smart home and mobile terminals, and discloses a smart home anti-theft system based on a smart mobile terminal, which comprises a dynamic behavior learning module, a multi-modal sensor collaborative network, a dual-channel verification unit and a cross-platform emergency linkage engine. According to the smart home anti-theft system based on the smart mobile terminal, a false alarm scene is effectively eliminated and the false alarm rate is reduced through a multi-modal sensor collaborative network and a dual-channel verification unit, and a personalized security policy is dynamically generated by adopting a mixed learning algorithm and an edge-cloud collaborative computing architecture, so that the privacy data of a user is protected; through the cross-platform emergency linkage engine, hierarchical alarm and multi-dimensional response are supported, the intrusion event processing efficiency is improved, it is ensured that an evidence chain cannot be tampered, judicial evidence obtaining standards are met, and the problem that evidence of a traditional system is prone to being lost or tampered is solved.

The invention provides an anti-cheating method and system based on game user behavior analysis, and the method comprises the steps: constructing a multi-dimensional behavior feature collection module, and collecting the operation behavior data of a game user in real time; based on the operation behavior data, constructing a user behavior basic model through a deep learning neural network; a multi-stage discrimination mechanism for behavior anomaly detection is established, and comprehensive credibility evaluation is obtained in combination with the basic model; implementing a self-adaptive risk grade division and dynamic response strategy, dividing users into different risk grades according to comprehensive credibility evaluation, and taking corresponding anti-cheating measures for different grades; a distributed collaborative verification network is constructed, encrypted transmission and cross verification are carried out on behavior feature vectors of suspicious users among a plurality of server nodes, the reliability of a verification result is ensured through a Byzantine fault-tolerant algorithm, and meanwhile user privacy data are protected. According to the method, the personalized behavior pattern of the user can be accurately captured, the accuracy of cheating detection is remarkably improved, and the misjudgment rate is reduced.

The invention discloses a network data sharing method and system based on dual identity authentication, and the method comprises the steps: a user A logs in a client A through the dual authentication of a password and a digital certificate, and transmits an encrypted file data packet formed after an original file is encrypted to a server; the user A selects target data at the client A and appoints a shared user B, and the server updates a shared file list of the user A; the user B submits a data downloading request on the client B, and the server verifies the authority of the user B and sends the encrypted file data packet to the client B; the client B decrypts the encrypted file data packet to obtain an original file; the system comprises a client module, a storage module and a server module. According to the method, a dynamic key generation mechanism is adopted, the requirement for real-time change of the authority in a multi-user cooperation scene is met, the leakage risk of a preset key is avoided, and the conflict between user privacy and sharing convenience is effectively solved.

The invention relates to the technical field of virtual reality interaction, and discloses a virtual reality multi-person interaction method, which comprises the following steps: realizing environment synchronous initialization based on space anchor point calibration and dynamic object loading, and configuring value physical rules and interaction logic; executing a data synchronization mechanism by establishing connection and session management; performing real-time action synchronization and interaction logic processing according to user input processing and action capture; user data privacy and security are protected, and content security and user behaviors are supervised. According to the virtual reality multi-user interaction method and system, by adopting a distributed server architecture and dynamically distributing the nearest edge node according to the geographic position of the user, dynamic load balancing can be realized, QoS priority division is started, low-delay transmission is realized by adopting a UDP + RUDP protocol, communication data is encrypted by adopting an AES-256-GCM, and a key is dynamically updated by adopting a dual-ratchet protocol; and the biological characteristics are converted into irreversible hash values, so that the security of privacy data of the user can be effectively improved.

The invention discloses a computer big data information security risk behavior identification method and system, and aims to solve the problem of sensitive information leakage existing in image information transmission in chat software. According to the method, when a user sends a picture to a target contact person, detail information in the picture, such as an environment scene, position information, text content, associated figures and clothes and accessories, is automatically recognized, and a sensitive information prompt list is generated for the user to view. And the user can select to cancel the sending, perform desensitization processing or confirm the sending according to the prompt list, so that the sensitive information is effectively prevented from being leaked. According to the method, the sensitive information in the picture is automatically identified and the user is prompted, so that the safety awareness of the user is enhanced, and the risk of sensitive information leakage is effectively reduced. And meanwhile, flexible desensitization processing options are provided, so that the privacy of the user is protected, and the individual requirements of the user are met.

The invention discloses a user privacy protection method and device for a face recognition system, and belongs to the technical field of mobile equipment and software security, and the method comprises the steps: carrying out the edge detection and lightweight semantic segmentation parallel processing of an input image, and generating edge strength mapping and semantic confidence mapping; generating a privacy grading mask through dynamic weight fusion edge strength mapping and semantic confidence mapping; dividing the image into three levels of privacy areas through a privacy grading mask; performing hierarchical encryption on the three-level privacy area through three non-periodic chaos sequences generated in advance to complete user privacy protection; wherein the three non-periodic chaotic sequences are iteratively generated through a Young's hyperchaotic system based on a Hash value spliced by a user-defined key and system time; according to the invention, picture encryption according to the privacy level is realized, and the user privacy in face recognition is protected.