296 results about "Secure multi-party computation" patented technology

A federated distributed computational system enables secure, privacy-preserving biological data analysis and engineering through interconnected nodes coordinated in a distributed graph architecture. A federation manager allocates resources, manages data flow and lineage, establishes privacy boundaries, and maintains cross-institutional knowledge relationships. Each node contains a processing unit for biological data analysis, privacy preservation protocols for secure multi-party computation, a knowledge graph structure with supporting data stores, and encrypted network connections. The federation manager enforces all computation and data exchange through secure channels while maintaining privacy, security, and contractual boundaries. This architecture enables research institutions to collaborate on complex biological analyses without compromising sensitive data, facilitating breakthrough discoveries through shared computational resources while maintaining strict data privacy and security controls.

The invention discloses a real-time health risk prediction method and system based on a dynamic knowledge graph, and relates to the technical field of medical information. The method comprises the following steps: carrying out multi-modal fusion and privacy protection preprocessing on medical and nursing heterogeneous data, and realizing semantic consistency of cross-mechanism data based on an entity alignment method of a cross-modal graph neural network; based on a hierarchical federated learning framework, local model parameters are subjected to hierarchical encryption and aggregation through a secure multi-party computing protocol to generate an initial global model, and prediction distribution of the global model is optimized in combination with knowledge distillation of differential privacy constraints; designing a gradient difference dynamic updating trigger mechanism of noise robustness, smoothing noise interference through a sliding window mean value, and realizing adaptive threshold calibration through linkage model performance verification; and light-weight deployment real-time reasoning is realized based on redundant edge pruning of confidence and 8-bit symmetric quantization. On the premise of protecting data privacy, the real-time performance and accuracy of health risk prediction are remarkably improved, and the method is suitable for a cross-institution medical care collaborative decision-making scene.

A federated distributed computational system enables secure, multi-institutional biological data analysis and genomic medicine through interconnected, decentralized nodes in a federated distributed graph architecture. A federation manager coordinates computational resource allocation, control and data flows, establishes privacy and security boundaries, implements multi-scale spatiotemporal analysis and simulation modeling, models cross-species or intrapopulation elements, and maintains cross-institutional knowledge relationships. Each node includes a local processing unit for biological data analysis, including multiomics and gene editing, privacy-preserving protocols for secure multi-party computation, a hierarchical knowledge graph for managing multi-domain biological relationships across spatial and temporal scales, and encrypted network connections. The system implements cross-species genetic analysis via phylogenetic integration, environmental response modeling through spatiotemporal tracking, and multi-scale tensor-based data integration with adaptive dimensionality control. This architecture enables research institutions to collaborate on complex biological analyses and genomic medicine applications while maintaining strict data privacy and security controls.

The invention discloses an access anomaly analysis method and system based on multi-dimensional features and user behaviors, and relates to the technical field of dynamic access anomaly detection, and the method comprises the steps: based on a dynamic hypergraph structure, extracting high-order correlation features of the user behaviors through a multilayer hypergraph convolutional network, and generating a high-order feature matrix; based on the high-order feature matrix, generating an authority approval threshold through a causal reinforcement learning framework, constructing a user behavior causal graph to generate strategy network parameters, and storing the strategy network parameters to distributed nodes of a regional data center; based on strategy network parameters stored by distributed nodes, security multi-party computing is adopted, cross-node collaborative optimization is carried out, and global defense strategy parameters are generated through a security aggregation algorithm. According to the method, security multi-party computing is adopted, cross-node collaborative optimization is performed, and the global defense strategy parameters are generated in combination with homomorphic encryption and a block chain fragmentation technology, so that the collaboration efficiency and strategy consistency among distributed nodes are improved on the premise of ensuring data privacy.

The invention discloses an intelligent fusion analysis system for multi-source heterogeneous data, and the system comprises a dynamic data collection module which is used for carrying out the data collection, and carrying out the processing of a collected mixed data flow; the semantic alignment module is used for constructing a domain ontology knowledge graph according to a preset scene target and carrying out semantic alignment and coordinate alignment on the collected data; the self-adaptive fusion engine module is used for fusing the collected multi-source heterogeneous data; the trusted computing module integrates a secure multi-party computing protocol and a homomorphic encryption algorithm to realize that data is available and invisible; and the intelligent decision-making module constructs a state action reward model based on reinforcement learning according to a preset scene target, and performs analysis and decision-making by using historical data and data acquired in real time. According to the invention, the capability and effect of data processing and decision support are improved.

The invention discloses an information security assessment method and system based on cloud computing, and relates to the technical field of information security, and the method comprises the steps: collecting multi-modal data, and carrying out the encryption through employing a node identifier, and generating a spatio-temporal data package; the method comprises the following steps: aggregating threat features by utilizing secure multi-party calculation through edge nodes, constructing a 58-dimensional square matrix, constructing a risk assessment model based on a quantum heuristic algorithm, positioning root cause threats by applying a Do-Calculus algorithm, dynamically adjusting and optimizing model weights by federal reinforcement learning, and outputting threat vectors to a cloud causal engine; the method comprises the following steps: constructing a space-time causal diagram by combining threat intelligence, positioning root cause threats by applying a Do-Calculus algorithm, generating a dynamic defense strategy, optimizing access control by KL divergence constraint, locally encrypting and storing a strategy execution log, and generating an evaluation audit log. According to the method, the problems of insufficient cloud computing multi-modal data relevance, defense lagging and verification fault are solved, and collaborative crossing of evaluation precision and response efficiency is realized.

The invention discloses a big data privacy protection modeling method and system based on federated learning and a block chain, and relates to the technical field of privacy protection and joint modeling. According to the method, homomorphic encryption, differential privacy, federated learning, secure multi-party computing and block chain technologies are fused, big data privacy protection and joint modeling are realized, encryption and dimensionality reduction are performed on original data through homomorphic encryption and differential privacy, an encrypted training sample of secure privacy is generated, a local model is trained on an encrypted data set through federated learning, and a big data privacy protection result is obtained. The method comprises the following steps: calculating aggregation parameters by using security multiple parties, constructing a verification network in combination with a block chain, ensuring credibility and integrity of model training, and finally, adding noise optimization performance for a global model by using differential privacy, testing generalization ability through cross validation, and determining a deployable privacy protection joint learning model, thereby breaking traditional data islands, promoting cross-mechanism data cooperation, and improving the privacy protection performance. Big data values are released, and data protection regulations and privacy requirements are met.

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 discloses a financial data processing method and system based on machine learning, and a storage medium. The method comprises the following steps: encrypting customer credit data of a financial institution through Paillier homomorphic encryption and extracting a risk feature vector; performing secret sharing segmentation on the local gradient parameters and then safely aggregating the local gradient parameters into global gradient parameters through a BGW protocol; on the basis of the privacy level, global gradient parameter differential privacy noise is injected and subjected to federal training to obtain a cross-institution credit evaluation result; calculating risk characteristics and evaluation results through homomorphic encryption to obtain an encrypted credit score, and performing secure multi-party calculation and decryption to generate a customer credit report; and block chain supervision compliance verification is carried out to generate an audit record, and a privacy budget optimization scheme is dynamically adjusted according to the business emergency degree. The technical problems that in an existing financial data processing method, information is incomplete due to data islands, data leakage risks are caused by insufficient privacy protection mechanisms, and an effective supervision compliance verification mechanism is lacked are solved.

This disclosure relates to a privacy preserving machine learning platform. In one aspect, a method includes identifying a request for processing an input feature vector by a machine learning model using a multiple multi-party computation (MPC) cluster including a plurality of MPC computing systems. Each feature of the input feature vector is encoded to generate an encoded weight vector. A respective share of the encoded weight vectors is generated for each computing system and provided to a corresponding computing system to generate a partial prediction for the respective share. The MPC cluster collects modified partial predictions for the input feature vector from the rest of the multiple MPC computing systems, where each of the modified partial predications is generated based on the partial prediction by a corresponding MPC computing system. A final prediction is generated by the MPC cluster based on the respective partial predictions.

The invention discloses a government affair data dynamic authorization management method based on secure multi-party computing, and relates to the field of government affair data security authorization management, and the method comprises the steps: distributing a unique identity identifier for each participant, generating an asymmetric key pair through employing a national secret SM2 algorithm, registering a public key and mechanism attribute information to an alliance chain smart contract, and generating an identity certificate package; and based on attribute information in the identity credential packet, the participants cooperatively generate anti-quantum dynamic attribute-based encryption key fragments through a secure multi-party computing protocol, and the anti-quantum dynamic attribute-based encryption key fragments are distributed to the participants by adopting a threshold secret sharing technology. Threshold decryption and token verification are achieved through an alliance chain verification intelligent contract, an authorization record is generated, an access control strategy of an edge computing node is configured according to the authorization record, an oblivious transmission protocol is adopted to respond to data query, and an audit node monitors an access log, dynamically adjusts attribute weight parameters and synchronizes the attribute weight parameters to a key fragment.

The invention discloses a cross-institution financial data federated learning modeling system and a privacy compliance verification method, and relates to the technical field of financial data processing, the system comprises a federated aggregation module used for a server to align financial time sequence characteristics of each institution through a timestamp hash alignment strategy, and a privacy verification module used for verifying the privacy compliance of each institution; aggregating the aligned financial time sequence characteristics by using a federated time convolutional network, and generating time sequence model parameters for capturing cross-mechanism global time sequence dependence; the federation reasoning module is used for generating a minimum spanning tree representation of a global enterprise association relationship based on a security multi-party computing strategy by the server side on the basis of a local sub-graph of the enterprise association relationship constructed by each institution client side; the server performs cross-mechanism graph reasoning by using a federated graph attention network and an aggregation graph embedding vector and combining minimum spanning tree representation, and constructs graph model parameters for identifying enterprise associated risks; and the model joint training module is used for the server to fuse the time sequence model parameters and the graph model parameters so as to determine global model parameters.

The invention discloses an emergency rescue data privacy protection and collaborative analysis system based on federated learning, and belongs to the technical field of emergency rescue data privacy protection and collaborative analysis. A dynamic trust evaluation module; the federated learning privacy protection engine module is used for realizing cross-domain data collaborative training by adopting a secure multi-party computing model; and the block chain intelligent contract module is used for deploying a data access control strategy and a credible auditing rule. According to the method, a global trusted environment is established through an end-side cloud three-level trust chain, the reliability of equipment is quantified through dynamic trust evaluation, data collaboration of privacy protection is realized through federated learning, an auditing strategy is automatically executed through an intelligent contract, an efficient encryption technology for guaranteeing safety and effectively reducing resource consumption is adopted, and the urban emergency data safety problem is solved.

The invention relates to the technical field of power system monitoring, and particularly discloses a cable current-carrying capacity dynamic evaluation and early warning system based on distributed sensing. The system comprises a distributed sensor network, edge computing nodes, a block chain privacy computing platform and a dynamic evaluation and early warning center. The sensing network collects multi-source physical parameters of the cable in real time; carrying out local preprocessing and feature extraction on the edge nodes; the privacy computing middle station cooperatively constructs and solves an electricity-heat-aging joint model and outputs a cable thermal state and an aging coefficient on the premise of not leaking special models and data of all parties through federated learning and a safe multi-party computing technology; and the early warning center calculates the dynamic current-carrying capacity, evaluates the health state and implements graded early warning. According to the invention, accurate and safe dynamic evaluation and intelligent early warning of the current-carrying capacity of the cable are realized.

The invention discloses a distributed privacy protection method and system based on federated learning, and relates to the related field of privacy protection, and the method comprises the steps: a hospital end carries out the homomorphic encryption of patient data, calls an initial model through the encrypted data, and carries out the incremental training, and generates a first model parameter set; sending to a central node, aggregating by using a secure multi-party computing protocol, and generating a first aggregation parameter group; after differential privacy noise is added, the data is transmitted back to a hospital end, local patient data is continuously called to carry out incremental learning after homomorphic encryption, and so on until model prediction precision meets constraints, and a target model parameter set is generated; and after differential privacy noise is added, the data is transmitted back to the hospital end for target model generation. The technical problems that existing privacy protection is large in calculation overhead and high in communication cost, and accurate aggregation of model parameters is difficult to achieve while privacy is guaranteed are solved, and the technical effects of small calculation overhead, low communication cost, privacy protection and accurate aggregation of the model parameters are achieved.

A collaborative transformation matrix learning system extends adaptive compression and encryption architectures through federated, privacy-preserving optimization. Each node analyzes local data distributions to generate anonymized distribution profiles using differential-privacy mechanisms, securely exchanging profiles and validated transformation matrices across a collaborative network. A trust and validation engine verifies mathematical properties and evaluates claimed performance metrics. Validated matrices are integrated into local optimization when trust and performance thresholds are satisfied. The system employs secure multi-party computation, homomorphic encryption, and conflict-resolution logic to ensure integrity of shared insights while preventing exposure of sensitive information. By combining collective learning with local adaptation, the invention accelerates convergence to optimal matrix configurations, mitigates cold-start inefficiencies, and improves compression-encryption efficiency and cryptographic strength across distributed deployments.

The invention discloses a multi-dimensional credit asset traceability system and method. The system comprises a data acquisition and encryption module (10) which is used for performing encryption and digital signature on credit asset data based on a trusted execution environment (TEE) and a hardware security module (HSM) at a data generation end; the parallel Hash processing module (20) is used for performing parallel Hash operation on the encrypted data and generating leaf node Hash values based on GPU acceleration and a multi-thread technology; the nested hash and zero-knowledge proof module (30) constructs leaf node hash values into a compression type Merkle tree and generates a zero-knowledge proof (ZKP), so that a third party can verify the authenticity and consistency of data without accessing full data; the on-chain evidence storage module (40) is used for recording root Hash (RootHash) and zero knowledge proof of the compression type Merkle tree to the block chain; the dynamic authorization and secure multi-party computing module (50) performs access clipping on the credit asset data based on the access intention and realizes verification of the minimum data dimension through secure multi-party computing (SMPC). Through combination of technical means such as trusted hardware acquisition, parallel hash calculation and privacy protection verification, rapid verification is realized on the premise of not exposing original data, and verification performance, data security and privacy protection capability are remarkably improved.

The invention provides a medical data-oriented efficient privacy computing fusion engine method and system, and the method comprises the steps: constructing a medical data encryption and decryption system as a basis, and carrying out the standardized packaging of federated learning, homomorphic encryption, secure multi-party computing and other technologies, a unified technical framework comprising a data encryption interface, a ciphertext calculation interface and a result decryption interface is defined; an intelligent task analysis mechanism is adopted, high-level medical analysis requirements can be automatically decomposed into privacy calculation subtask sequences, and multi-technology collaboration is achieved through an execution engine; meanwhile, the functions of resource monitoring and performance analysis are achieved, technology switching and parameter adjustment and optimization during operation are supported, and a self-optimization execution environment is formed. And finally, displaying through a friendly application interface and visualization. According to the method, the problems of single technical route, low calculation efficiency, poor resource utilization rate, serious memory limitation and the like in medical data privacy protection are solved.

The invention discloses an incomplete information game intelligent processing method and system based on secure multi-party computing, a terminal and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining a game participant set, an initial game state, a historical action set, a terminal historical set, and a preset game strategy based on a secure near-end strategy optimization algorithm, an action set, an information set and a revenue function corresponding to each participant are obtained; according to the participant set, the initial game state, the historical action set, the terminal historical set, the game strategy, and the action set, the information set and the revenue function corresponding to each participant, carrying out modeling based on a modeling framework of an extended game tree, and constructing a security extended game model; and according to a preset optimization target and leaf nodes in the security extension type game model, determining a game result between the participants and outputting the game result. Therefore, the data security can be improved, and the security of the game process can be improved.

The invention provides a multi-tenant multi-user login method based on enhanced OAuth2, which relates to the technical field of network security, and comprises the following steps: obtaining a key threshold through a tenant identifier, and executing OAuth2 authentication to obtain an access token; tenant fingerprint information is generated through zero-knowledge proof and homomorphic encryption, and a tenant verification token is generated through secure multi-party calculation; constructing a reinforcement learning access controller in combination with the historical access data, and generating and verifying an authority list; and finally forming a session identifier and storing the session identifier in a distributed cache. According to the invention, the authentication security is improved, cross-tenant fine-grained authority management is realized, and unauthorized access is effectively prevented.

The invention relates to the technical field of data sharing privacy protection, in particular to a cross-industry universal data sharing privacy protection method and a cross-industry universal data sharing privacy protection system. Through data request and identity authentication, generation of a mixed data set, modification of a homomorphic encryption algorithm, data segmentation, ciphertext distribution and secure joint modeling, secure sharing and joint modeling of data between cross-industry enterprises are realized. On the premise that data privacy is guaranteed, cross-industry data value is fully exerted, integration and innovation of industries such as medical treatment and finance are assisted, industry data sharing requirements are met, and industrial collaborative development is promoted.

Example computer-implemented methods and systems for secure random noise generation are disclosed. One example method includes generating, by a first party, n random first bits and n -bit strings. The first party generates, based on the n -bit strings and the n random first bits, n pairs of -bit input messages. The first party performs n 1-out-of-2 oblivious transfers (OTs) of the n pairs of -bit input messages from the first party to a second party. The first party generates, based on the n -bit strings, a first random number. The first party performs, based on the first random number, secure multiparty computation (MPC) that involves the first party and the second party.

The invention discloses a quantum security multi-party computing method of a hybrid topological structure, which is applied to a quantum security multi-party computing system, the quantum security multi-party computing system comprises m sub-networks of star network topological structures, and each sub-network corresponds to a quantum center and a plurality of users; quantum communication is completed between every two quantum centers based on a ring network topology structure; the quantum security multi-party computing method comprises the following steps: each quantum center completes modular addition operation of a corresponding user secret value in a corresponding subnet to obtain a modular addition result of all user secret values in the corresponding subnet, and the modular addition result is recorded as a subnet secret value modular addition result; and on the basis of a subnet secret value modular addition result, each quantum center continuously participates in the secure multi-party computing protocol among the quantum centers to obtain the sum of all user secret values in the quantum secure multi-party computing system. According to the method, on the premise that private secrets of users are not leaked, safer and more efficient quantum security multi-party calculation can be realized among the users with limited quantum capabilities.

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 data security, in particular to a federated learning-based distributed privacy data security aggregation method, which comprises the following steps of: acquiring multi-source heterogeneous data of a leaf supplier node; performing weighted fusion on the multi-source heterogeneous data by using the multi-modal fusion model to generate a health state vector; training by using the health state vector in combination with a blade X-ray defect data set to obtain a local model; calculating a leakage risk score, and performing privacy processing on the local model through a differential privacy mechanism to obtain a noise adding model; calculating the reference data set by using a noise adding model to obtain a performance score, and generating an encryption proof for a calculation process; and the central server receives the zk-SNARK encryption proof submitted by all the nodes, converts the performance score into an aggregation weight, initiates a secure multi-party computing protocol, and performs weighted calculation on the noise adding model by using the aggregation weight to generate a global model. According to the method, multi-party safe and credible collaborative modeling is realized by fusing multiple cryptographic protocols.

Embodiments of this specification provide computer-implemented methods, apparatuses, computer-readable media, and systems for secure multi-party computation. In an example secure multi-party computation method, a first party encrypts a first plaintext segment of target data by using a homomorphic encryption algorithm based on a public key held by the first party in a first key pair to obtain a first ciphertext segment. A second plaintext segment of the target data is owned by a second party. The first party sends the first ciphertext segment to the second party. The second party performs a homomorphic addition operation in the homomorphic encryption algorithm on the first ciphertext segment and the second plaintext segment of the target data to obtain ciphertext data of the target data. The ciphertext data is decrypted based on a private key in the first key pair.

The invention relates to the technical field of natural language processing, and discloses a special disease knowledge spectrogram construction and analysis system, which comprises a federal data acquisition module, a dynamic ontology mapping preprocessing module, a cross-source knowledge collaborative extraction module, a self-adaptive graph construction module, an intelligent dynamic updating module and an interactive analysis application module, the cross-source knowledge collaborative extraction module is used for collaborating multi-source data on the premise of guaranteeing data privacy, the dynamic ontology mapping preprocessing module is used for mapping knowledge representation of the multi-source data to a unified standard, and the cross-source knowledge collaborative extraction module is used for extracting medical entities and semantic relationships based on multi-modal data fusion. According to the method, through the cooperation of the longitudinal federated learning framework and the secure multi-party computing algorithm, the problem of multi-source medical data privacy collaborative computing is solved, and the advantages of safely aggregating multi-mechanism data and providing original data support for map construction on the premise of ensuring data privacy are achieved.

The invention discloses a secure multi-party computing method and system based on secret sharing, and relates to the technical field of information security, and the method comprises the steps: obtaining original computing data, and dividing the original computing data into a plurality of data fragments; homomorphic encryption and symmetric encryption are respectively and sequentially carried out based on all the data fragments to obtain a plurality of layered encryption fragments; generating a random mask value and correspondingly binding the random mask value with the layered encryption fragment to obtain a final encryption fragment; based on all the final encryption fragments, distributing the final encryption fragments to each computing node for security computing, and correspondingly obtaining an intermediate result and a corresponding zero-knowledge proof; performing verification based on all zero-knowledge proof, and aggregating corresponding intermediate results based on verification results to generate an encryption calculation result; and decrypting based on the encrypted calculation result to obtain a plaintext calculation result. And the calculation efficiency is improved on the basis of ensuring the safety of the calculation data.

The invention provides a zero-trust-based dynamic data secure transmission and processing channel construction device and method, and the method comprises the steps: S1, collecting to-be-transmitted data, and classifying the data to obtain a data classification result; s2, based on the data classification result, the user identity information and the environmental risk information, a dynamic security policy is generated, and the dynamic security policy comprises an encryption algorithm selection rule, a trust evaluation threshold and a data operation authority policy; according to the invention, the fine-grained security policy is generated and continuously adjusted by dynamically sensing the data sensitivity and the real-time trust state, so that the full-link self-adaptive security protection from transmission to processing is realized; according to the method, the security multi-party calculation is seamlessly integrated in a high-sensitivity scene, so that the end-to-end security and privacy protection level of data in a cross-domain and multi-participant environment are remarkably improved while the data mobility is guaranteed, and the problem that a traditional static security mechanism is difficult to adapt to dynamic risks is effectively solved.

The invention relates to a cross-domain security multi-party computing method, system and device based on the Internet of Vehicles and a medium. In the scheme, the edge sensing layer acquires the original data from the vehicle terminal, and the original data can be processed through the current privacy protection level, so that the privacy and efficiency are dynamically balanced through the current privacy protection level, and the calculation efficiency is improved on the basis of ensuring the data security; according to the method, the processed original data can be encrypted through the trusted execution environment, so that the security of the data processing process and the original data is ensured; moreover, after the SMPC coordination layer performs joint calculation on the target data through the SMPC protocol and the current privacy protection level, the joint calculation result is uploaded to the block chain notarization layer, and the block chain notarization layer verifies and records the result to the block chain system, so that the integrity and non-tampering property of the joint calculation result can be further ensured.