1762 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 present invention relates to systems and methods for integrating real-world functionalities, such as healthcare services, into a virtual environment (metaverse) using digital avatars and data security measures. Users can access services through their avatars, maintaining anonymity and data security. The system securely transmits user requests and data to real-world service providers, who provide services or feedback within the virtual environment. Data security ensures confidential and tamper-proof data transmission and facilitates secure payment transactions. The invention enables the integration of various real-world services into the metaverse, enhancing its functionality and utility while preserving user privacy and security.

The invention discloses a user data intelligent protection method and system based on differential privacy, and belongs to the technical field of data protection, and the method specifically comprises the steps: collecting and preprocessing user health data; setting a dynamic privacy budget according to the data sensitivity, the analysis purpose and the privacy demand, and adding random noise to generate disturbance data by adopting a multi-strategy fused data disturbance strategy; statistical analysis is carried out, and an improved differential privacy algorithm is used to assess the privacy leakage risk, the protection degree and the data availability; based on an evaluation result, a privacy budget and a disturbance strategy are iteratively adjusted through a feedback loop mechanism; the health data analysis method provided by the invention not only can protect user privacy, but also can ensure data availability.

The invention provides a user opinion privacy protection method and system based on federated learning and privacy difference, and relates to the technical field of privacy protection, and the method comprises the steps: a server receives a client request, divides a data set, and distributes the data set; the client performs local training by using a differential privacy machine learning algorithm, and disturbs model parameters by using Laplacian noise; the server uses secret sharing and homomorphic encryption technologies to aggregate parameters and update a global model; and the client receives the privacy protection strategy, carries out privacy processing on the user opinions and then sends the user opinions to the server for analysis. The method effectively protects user privacy and improves data utilization efficiency.

The invention belongs to the technical field of Internet of Vehicles, and particularly provides a federated learning optimization system and method for an intelligent network connection automobile scene, which improves an asynchronous federated learning framework, divides a plurality of vehicles into different clusters due to the isomerism of computing power, and adopts a cloud-edge-end three-layer federated learning framework, so as to realize the optimization of the federated learning of the vehicles. An edge server is deployed on a roadside unit RSU, received model parameters are subjected to pseudo-asynchronous aggregation in a self-adaptive dynamic time window, the received model parameters are subjected to real-time aggregation at a cloud end, the system model training efficiency and quality are improved, meanwhile, the data security is ensured, the user privacy is protected, and the user experience is improved. And the efficiency of the federated learning optimization system and the quality of the global model in the intelligent network connection automobile scene are ensured.

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 progressive privacy calculation method and system for a virtual-real fusion scene of an intelligent sweeper, and relates to the technical field of sweepers, and the method comprises the steps: collecting indoor space data through a multi-modal sensor, and constructing a scene knowledge graph through an entangled state registration network and a graph neural network; then, based on deep reinforcement learning and zero-knowledge proof, hierarchical encryption is carried out on the atlas, and scene data versions with different privacy protection intensities are generated through federated learning; and finally, performing path planning in a secure multi-party computing environment, and optimizing a cleaning strategy by using neural symbol reasoning and a graph attention network. According to the invention, scene understanding and path planning of the intelligent sweeper can be realized on the premise of guaranteeing the privacy of the user, the sweeping efficiency is improved, and the user experience is improved.

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 federated learning model training method and system based on differential privacy image noise addition. The method comprises the following steps: S1, initializing an evaluation network, a target network and hyper-parameters; s2, initializing a global model and parameters; s3, acquiring a selected client and a privacy budget; s4, the selected client performs local training and uploads the disturbance gradient to the central server; s5, the central server aggregates the disturbance gradient of the client to obtain an updated global model and a reward corresponding to the current state; s6, the next state of federated learning is obtained, and the evaluation network and the target network are updated; s7, repeatedly executing the steps S3 to S6 until the set number of global iterations is reached; and S8, updating the exploration rate and repeatedly executing the steps S2 to S7 until the set federal learning times are reached. The invention provides a dynamic differential privacy method in combination with a federal learning framework, which can improve the image recognition rate while ensuring the privacy of the user.

Various embodiments include systems and methods for generating a privacy-aware multi-modal autoreply to an incoming communication. A processing system of a computing device may collect multi-modal information, determine a current user circumstance based on the collected information, determine a user privacy preference for autoreply responses, and generate a prompt that is input to a generative large language model (LLM) to generate optional autoreply responses, receive a list of personalized response suggestions from the generative LLM, and perform an autoreply action based on a selected personalized response suggestion.

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 discloses a large model training method, medium and system based on a differential privacy mechanism, and the method comprises the steps: S101, obtaining historical data, carrying out the preprocessing, and carrying out the grouping and marking of the preprocessed historical data, so as to form a training data set; s102, initializing parameters of the large language model; s103, performing training based on the training data set, and calculating a gradient; s104, performing gradient noise addition to obtain a noise addition gradient, and calculating a corresponding comprehensive unfairness index value based on the noise addition gradient; s105, judging whether the comprehensive unfairness index value is within a preset value range or not; s106, if the comprehensive unfair index value is within a preset value range, judging whether the current large language model meets a training requirement or not; if yes, taking the current large language model as a final model; and if not, returning to the step S103. According to the invention, the privacy of the user can be effectively protected, and meanwhile, the unfair phenomenon caused by the use of a differential privacy mechanism is avoided.

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 provides an edge generation type large language model collaborative reasoning method for a ubiquitous computing power network. The edge generation type large language model collaborative reasoning method comprises the steps that generation type large language model information and edge device information are obtained; carrying out parallel reasoning modeling and solving on edge equipment assembly line arrangement and a generative large language model assembly line; performing parallel pre-filling based on an assembly line divided by the input sequence; the invention relates to assembly line parallel autoregression decoding based on an outline. According to the method, by means of computing resources of multi-edge-end equipment in a ubiquitous computing power network environment, a pre-filling stage of the generative large language model is accelerated through a sequence division-based pipeline parallel reasoning technology, and meanwhile, an autoregression decoding stage of the generative large language model is accelerated by utilizing an outline-based pipeline parallel decoding method; low-delay and resource-efficient distributed collaborative reasoning of the generative large language model on multiple edge devices is realized; the privacy data security of the user is effectively protected, and the method has better adaptability and robustness aiming at the resource characteristics of the complex edge environment.

The invention discloses a human face sensitive data protection method based on an elastic anti-interference privacy protection mechanism, belongs to the field of information processing, and provides a dynamic self-adaptive sampling strategy based on information entropy in a data preprocessing stage, and the sampling rate is adjusted according to the distribution characteristics of a human face data set and the weight of sensitive information. And the model performance is maximized on the premise of ensuring user privacy. In order to obtain a better privacy protection effect, an entropy adjustment differential privacy mechanism is designed, and Gaussian noise of different degrees is adaptively added according to an information entropy value of face data after a face data set is dynamically and adaptively sampled, so that refined privacy protection of face sensitive data under differential privacy constraint is realized. In a model training stage, an elastic anti-interference training method is provided, diversified adversarial samples are dynamically generated in different stages of model training, and the over-fitting risk of the model is reduced while the robustness of the model to various types of adversarial attacks is enhanced.

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 privacy protection-oriented personalized federal recommendation system and method, and relates to the field of federal recommendation systems. According to the method, an article preference embedding vector is constructed for each client, and a global article embedding orthogonal mapping matrix and a double-layer perceptron used for generating an article embedding mixed factor matrix are trained, so that the global article embedding matrix and the article preference embedding vector of a user are fused; a mixed article embedding matrix which is more personalized and finer in granularity is constructed for the client, and the mixed article embedding matrix is input into a personalized recommendation prediction perceptron constructed for the client to obtain a recommendation result, so that the final recommendation effect is improved; when the client side uploads data to the central server, local differential privacy encryption is carried out; according to the technical scheme, negative sampling is carried out on each client side, the positive samples and the negative samples are mixed and then jointly used for training, the training effect is improved, meanwhile, the server cannot distinguish the historical interaction situation between the client sides and the articles according to the update data uploaded by the client sides, and user privacy is protected.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboratively training an interaction prediction machine learning model using a plurality of user devices in a manner that respects user privacy. In one aspect, the machine learning model is configured to process an input comprising: (i) a search query, and (ii) a data element, to generate an output which characterizes a likelihood that a given user would interact with the data element if the data element were presented to the given user on a webpage identified by a search result responsive to the search query.

The invention relates to the technical field of online education, in particular to an intelligent learning scene pushing system based on a spatio-temporal sequence, which comprises a spatio-temporal data acquisition module, a spatio-temporal feature analysis module, a learning scene recommendation module, a feedback collection module, a self-adaptive optimization module and a data security protection module, the spatio-temporal data acquisition module is used for acquiring learning time and spatial data of a user in real time, the spatio-temporal feature analysis module extracts a user learning rule based on data analysis, the learning scene recommendation module pushes learning content suitable for a current environment according to an analysis result, and the adaptive optimization module dynamically adjusts a learning strategy according to user feedback. Personalized learning experience is ensured, and the data security protection module guarantees user privacy and data security through encryption and authority management; according to the invention, accurate and efficient learning pushing can be realized under different learning situations, and the method is suitable for the fields of online education, intelligent learning platforms and the like.