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

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

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 cross-industry data security sharing method and system based on data desensitization and a medium, and relates to the related field of privacy computing, and the method comprises the following steps: carrying out dynamic desensitization processing on original data, generating a shared data set for real-time recording, constructing an access behavior characteristic spectrum to configure a dynamic access strategy, and establishing a third-party security data sharing platform, storing the shared data set in a trusted execution environment in a fragmented manner, obtaining fragmented encrypted data, executing a dynamic access strategy, performing conjoint analysis on the fragmented encrypted data through a security multi-party computing protocol, and generating a data security sharing suggestion. The technical problems that in cross-industry data sharing, the privacy leakage risk is high, the static desensitization rule is rigid, and the multi-party calculation efficiency is low are solved, and the technical effects of breaking data islands and constructing safe and mutual-trust data circulation ecology through fusion of dynamic hierarchical desensitization and a trusted execution environment are achieved.

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 provides a blockchain-driven distributed private data storage and access control method and system, and relates to the technical field of access control, and the method comprises the steps: carrying out the sensitivity scoring and fragmentation processing of private data, organizing a data fingerprint into a Bloom filter, and constructing an integrity proof tree, the data fragments are encrypted and then submitted to a distributed storage network, and an evidence storage record of the data fingerprints is generated in the block chain; during data access, a hierarchical access key is dynamically generated according to behavior characteristics of a visitor, and the hierarchical access key is distributed to an authorized node group after threshold segmentation; the authorization node group generates an authorization voucher by using a threshold signature scheme based on a lattice password, and writes the authorization voucher into the block chain; and the visitor obtains the data fingerprint and the encrypted data fragment by using the authorization certificate, and decrypts the data fragment of the corresponding authority level through the secure multi-party calculation recombination key component after verifying the integrity.

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 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 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.

System and method of creating a multi-party computation (MPC) cryptographic signature for a blockchain based computer network, including: generating at least one first share and second share of a cryptographic key, based on a distributed key generation MPC protocol, signing a received message with the at least one first share, receiving the message signed with the at least one first share, signing the message signed with the at least one first share with the at least one second share, sending the message signed with the at least one second share and the at least one first share to a full node of the computer network, and adding a transaction to a ledger of the computer network, in accordance with the received message signed by the at least one first share and the at least one second share.

The invention provides a privacy protection-based graph data constraint shortest path verification query method and system, and the method comprises the steps: obtaining a path query request sent by a user side, carrying out the encryption processing of target graph data, generating an encrypted graph structure feature, and segmenting the encrypted graph structure feature into a first encrypted sub-feature and a second encrypted sub-feature; calling a pre-trained key segmentation model to match the path constraint condition with a preset privacy policy, generating a verification token, sending the first encryption sub-feature and the verification token to a first computing node through a distributed protocol, and sending the second encryption sub-feature and the verification token to a second computing node, and triggering the first computing node and the second computing node to execute secure multi-party computing, outputting the candidate path ciphertext and the verification parameter set, performing consistency verification, and if the verification is passed, performing decryption processing to generate a target shortest path meeting a path constraint condition. According to the method, a solution which takes privacy protection, result accuracy and calculation efficiency into consideration is provided for a high-security scene.

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.

The invention relates to a digital twinborn factory data communication and privacy protection method, which comprises the following steps: establishing an edge-cloud digital twinborn model which comprises an edge layer, a digital twinborn layer and a cloud layer; the edge layer comprises an edge server and IIOT equipment of a factory; the digital twin layer comprises a plurality of digital twin servers; the cloud layer comprises a cloud server; dividing the neural network model for classifying tasks into a shallow model and a deep model; and the cloud server receives real-time model update provided by the digital twin server, grouping and training the IIOT equipment based on the digital twin model updated in real time, weighting and aggregating local shallow model parameters encrypted by each IIOT equipment in a group by adopting a secure multi-party computing method, and performing parallel computing by the IIOT equipment by using the aggregated part. According to the method, data sharing among multiple pieces of workshop equipment can be realized while data privacy is protected, the production process is optimized, and the product quality is improved.

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.

Disclosed herein are systems and methods for distributed processing of secure multi-party computations. One example method includes dividing an overall SMPC circuit into partitions to be processed by different computers, balancing the computational load across the computers by ensuring the partitions are of similar sizes, identifying and minimizing a number of inputs and outputs crossing partition boundaries to optimize performance and employing a controller computer to orchestrate distributed computational tasks and recover from potential system failures.

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 data perception control, and provides a building user big data and AI perception combined intelligent elevator automatic control method, which comprises the following steps: deploying a distributed network at a building, obtaining elevator perception data, encrypting, fusing and partitioning the elevator perception data, and obtaining a building user big data and AI perception combined intelligent elevator automatic control system. Feature extraction is carried out based on federated learning of secure multi-party calculation, and fused perception features are generated; a space-time diagram is constructed according to fused sensing features, elevator taking demand modes of different building types in different scenes are learned and predicted through a space-time diagram convolutional neural network and a generative adversarial network, operation areas are flexibly divided based on the building types and the scenes, and operation routes are dynamically planned through a partition scheduling strategy. The cross-regional collaborative scheduling of the elevator is realized; and elevator abnormal states including passenger overload, abnormal behaviors, elevator car environment abnormity and the like are detected in real time according to the fused sensing data, multi-stage abnormity classification is carried out, and a corresponding emergency control strategy is triggered based on a classification result.

Methods and systems for training machine learning models with enforced differential privacy using Secure Multi-Party Computation (SMPC) protocols are disclosed. A method for generating random bits for a Secure Multi-Party Computation (SMPC) protocol in a machine learning model training system includes locally generating, at each participating node, a plurality of random bits using a cryptographically secure pseudorandom number generator (CSPRNG). The locally generated random bits provided as input to the SMPC protocol and combined using an exclusive or (XOR) or addition operation to produce a set of combined random bits. A statistical sampling process for Differential Privacy (DP) is performed using the combined random bits as inputs. The perturbed training data is then utilized in the SMPC protocol to train the machine learning model.

The invention belongs to the field of passwords, and discloses an anti-quantum threshold digital signature method and system capable of tolerating high network delay. The method comprises the following steps: in a secret key generation stage, a trusted third party randomly selects a secret key and performs Shamir secret sharing on the secret key, and distributes a secret sharing value to each signer; in a distributed signature stage, each signer carries out packaging and secret sharing on a secret sharing value, a proving sub-circuit of a public key is operated based on a secure multi-party computing protocol, and each participant outputs a signature through two rounds of interaction. The anti-quantum threshold digital signature scheme has function interchangeability, that is, the signature generated by the threshold scheme and the signature generated by the original signature scheme can be verified by the same verification algorithm; meanwhile, the threshold digital signature scheme is efficient and achievable, and a threshold signature protocol in a scene of two parties of a wide area network only needs 2 seconds; moreover, the method has a better number of rounds, and the signature algorithm only needs two rounds.

The present disclosure relates to methods and systems that preserve privacy in a secure multi-party computation (MPC) framework in multi-agent reinforcement learning (MARL). The methods and systems use a secure MPC framework that allows for direct computation on encrypted data and enables parties to learn from others while keeping their own information private. The methods and systems provide a learning mechanism that carries out floating point operations in a privacy-preserving manner.

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 decentralized hierarchical federated learning method and system, and edge servers. The method comprises the following steps: a plurality of edge servers respectively receive gradient ciphertexts sent by different participant clients; the plurality of edge servers aggregate the received gradient ciphertext based on a secure multi-party computing protocol to obtain an aggregation model ciphertext; according to the secure multi-party computing protocol, in a non-trusted multi-user network, a plurality of edge servers respectively holding different gradient ciphertexts jointly compute aggregation model ciphertexts based on the gradient ciphertexts, and each edge server only obtains part of data in the aggregation model ciphertexts. The gradient ciphertext held by the edge server is not leaked to other edge servers; and the edge server issues the aggregation model ciphertext to the participant client. By using the scheme of the invention, the security of model aggregation can be improved.