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

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.

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 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.

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 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 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.

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 provides a federal learning optimization method and system for multi-source heterogeneous cardiovascular disease risk prediction, and relates to the technical field of data processing, and the method comprises the steps: processing original cardiovascular disease data, and obtaining standardized feature data; based on the standardized feature data, a differential training algorithm corresponding to the data structure of the standardized feature data is adopted for local calculation, and updated encrypted local parameters are generated; the updated encrypted local parameters are transmitted to a parameter server in a federated learning framework, the parameter server adopts a geometric mean algorithm based on secure multi-party calculation to perform secure aggregation and geometric mean integration on local parameter updates from different sources in an encryption state, and a preliminary global parameter set is generated. According to the method, data privacy protection is enhanced through a federated learning framework, the multi-source heterogeneous data fusion efficiency is improved, and parameter integration is optimized to enhance model performance.

The invention belongs to the field of data security, and particularly relates to a time sequence encrypted storage and expired crushing method for monitoring data of external equipment, which comprises the following steps: acquiring a monitoring data stream of the external equipment, segmenting the monitoring data stream according to a preset time window, and deriving a unique encryption key for each segment; encrypting and storing the corresponding window data by using the key to obtain a time sequence encrypted data block; when the conjoint analysis request is responded, a specified window secret key is distributed to a calculation party according to a preset authorization strategy, so that the calculation party executes secure multi-party calculation on the window data block; meanwhile, the life cycle of the data block is monitored in real time, data is automatically erased when the data block expires, a corresponding secret key is destroyed, and a shredded verifiable evidence is generated and uploaded to a storage chain constructed by combining a root secret key of the general data platform and secret keys of all calculation parties with a block chain; according to the application, data storage security and conjoint analysis compliance are guaranteed, the risk of residual keys is avoided, the traceability of crushing operation is ensured, and the credibility of cooperation of multiple participants is improved.

The invention discloses an end-end self-control-based transformer area light storage and charging coordinated regulation and control system and an autonomous method. The system constructs an end-end-transformer three-level autonomous architecture: an end-level autonomous unit is responsible for local millisecond-level reflection control; the edge autonomous nodes realize intra-zone collaboration through federated learning, and physical security constraints are embedded into AI decisions by using digital twin bodies; the court-level federation learning center realizes knowledge sharing under cross-court privacy protection through secure multi-party computing, the method further integrates transfer learning to solve the cold start problem of a new court, and a three-time scale regulation and control mechanism is adopted to cope with disturbance of different rates; the autonomous capability, the response speed, the safety level and the intelligent degree of the system are improved.

The invention discloses a privacy protection Transform reasoning method based on function secret sharing, which is characterized in that homomorphic encryption is adopted to encrypt an input token sequence, nonlinear operation is processed through a secure multi-party computing protocol, a distributed interval function key is generated by using function secret sharing, and secure comparison and dynamic token pruning are realized. The privacy protection reasoning specifically comprises the steps of an initialization stage, an input encryption stage, a cooperative calculation stage, a safety pruning stage, a result recovery stage and the like. Compared with the prior art, the method has the advantages that the online communication complexity of security comparison is reduced to a constant level from a quadratic level through a function secret sharing technology, the communication bottleneck problem of token pruning in the prior art is effectively solved, and the security comparison efficiency is improved on the premise of protecting client input data and server model parameter privacy. The reasoning efficiency of the Transform model in the privacy sensitive scene is remarkably improved, the method is particularly suitable for the scene where Transform model reasoning needs to be carried out on sensitive data and the privacy requirement is high, and the application prospect is good.

This application provides a digital resource processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product, belonging to the field of data processing technology. The digital resource processing method includes: associating and storing an encrypted aggregated digital resource value with a target cross-platform identity identifier of a target object; the encrypted aggregated digital resource value is generated by a secure multi-party computing cluster in encrypted state through collaborative computation of encrypted digital resource values ​​provided by several platforms; upon receiving a query request and confirming that the requester is the holder of the target cross-platform identity identifier, retrieving the encrypted aggregated digital resource value and sending it to the secure multi-party computing cluster for collaborative decryption, returning the decrypted aggregated digital resource value; and responding to the requester's digital resource exchange request by executing a digital resource exchange operation. The technical solution of this application can effectively protect data privacy while jointly using digital resources from multiple platforms.

The invention relates to the technical field of internetwork settlement, in particular to an account checking method and device based on a block chain oracle machine and secure multi-party computing, computer equipment, a non-temporary computer readable storage medium and a computer program product. According to the method, a credible, automatic and transparent scene on a chain is provided by adopting a smart contract based on a block chain, the contradiction between account checking data privacy and verifiability among operators is solved through privacy perception by adopting secure multi-party calculation, and meanwhile, reverse operation is performed on an oracle machine; complex calculation in the smart contract is put into an out-of-chain oracle machine operation, and finally a difference vector set of account checking is accurately obtained, and then whether internetwork settlement continues to be carried out is judged. On the premise of guaranteeing the privacy, credibility and safety of the account checking data through cooperation of the three, the defects that the account checking efficiency is low, the account checking process is not supervised, and the account checking result is not subjected to closed-loop feedback are overcome.

This invention provides a cross-institutional collaborative data computation method based on secure multi-party computation, belonging to the field of data security and privacy protection technology. The method includes: each participating institution registering its identity and data metadata; the task initiator publishing a computation task description; performing operator-level decomposition on the objective function to obtain efficiency, overhead, and security strength indicators for each candidate protocol; using the security strength indicators to correct the efficiency and overhead indicators, weighted summing to obtain a comprehensive score, selecting the optimal protocol accordingly, and generating a hybrid protocol graph and execution plan; performing collaborative computation in stages, with protocol conversion at transition points; finally, the share is sent to the settlement node, aggregated and reconstructed to obtain plaintext results, and generating verifiable proofs. This invention achieves dynamic adaptation of protocols and tasks, optimization of security and performance balance, verifiable results, and dynamic adaptive scheduling.

The present application belongs to the cross field of information security and computer application technology, and relates to a large model privacy protection reasoning method and system based on secure multi-party computation. The method comprises: obtaining a prompt required for reasoning in a secret sharing form input by a client; obtaining a large model weight parameter input in a secret sharing form by a server; performing privacy protection calculation on each operation in the large model and executing model reasoning; sending a shared value of the model reasoning result to the client, and reconstructing the reasoning result based on the shared value by the client. The present application uses homomorphic encryption, vector oblivious linear evaluation and other cryptography technologies and segmented polynomial optimal approximation to construct a secure reasoning framework of a large Transformer model, can perform fast and accurate large model collaborative reasoning, and provides semi-honest security for the client and the model owner, that is, both parties need to accurately comply with the agreement and the privacy of each party will not be leaked.

The present disclosure provides a kind of SQL-like query processor for secure multi-party computing.The overall structure is: design SQL-like parser for receiving SQL-like statement, then through adapter, framework adaptation is converted into logical plan and input SQL optimizer, then the physical plan obtained is input task generator, the optimized physical plan is decomposed, they are converted into task sequence TaskDAG convenient for scheduler on chain distribution, and TaskDAG is input secure multi-party operator;The underlying implementation of the long chain is adapted to the blockchain application in privacy computing technology, a Task generator is independently developed, and the optimized execution plan is decomposed into TaskDAG suitable for blockchain environment scheduling and parallelism;At the same time, considering the communication and computing overhead between different nodes, to further improve the effect of execution efficiency and performance.

The application provides a method and system for multi-party secure calculation of coefficient of variation, belonging to the field of multi-party secure calculation, and used for providing a new scheme for achieving the task of securely calculating the coefficient of variation of a data group, wherein the multi-party includes an initiator of a calculation task, a referee and a plurality of participants, the participants and the initiator hold private data, the initiator and the referee are two different parties, and the method comprises the following steps: the participants pre-process the data, the referee generates a public key and a private key based on a self-developed homomorphic encryption algorithm, and sends the public key to the participants, so that the private data of all the participants is encrypted, all the participants collect the encrypted data to the initiator, the initiator calculates an encrypted sum, the referee decrypts the sum and sends it to the initiator to obtain a mean value, all the participants calculate variance elements according to the mean value, and the variance elements are segmented, recombined and collected to the initiator, so that the initiator can calculate a standard deviation and then calculate a coefficient of variation.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using cryptographic techniques to enhance data security and privacy and increasing computational efficiency in selecting digital components for multiple digital component slots are described. In one aspect, a method includes receiving, from a client device and by a first MPC computer of a group of MPC computers that collaborate to perform MPC computations, a composite request for digital components to display in multiple digital component slots of an electronic resource. The composite request includes first secret shares of data identifying user groups that include a user of the client device as a member. A determination is made, in collaboration with one or more second MPC computers, a first secret share of a value of each of multiple candidate parameters of a candidate expression for each digital component in a set of digital components.

The application belongs to the field of data security, and particularly relates to a time-sequencing encryption storage and expiration pulverization method of external device monitoring data, comprising: obtaining an external device monitoring data stream, segmenting according to a preset time window and deriving a unique encryption key for each segment; encrypting the corresponding window data with the key and storing to obtain a time-sequencing encryption data block; in response to a joint analysis request, distributing a specified window key to a calculation party according to a preset authorization strategy for it to perform secure multi-party computation on the window data block; simultaneously, monitoring the data block life cycle in real time, automatically erasing the data and destroying the corresponding key when expired, generating a pulverization verifiable evidence, and uploading to a storage chain constructed by a general data platform root key and each calculation party key combined with a blockchain; the application guarantees data storage security and joint analysis compliance, avoids residual key risk, ensures pulverization operation traceability, and improves multi-party cooperation credibility.

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 present disclosure provides a multi-administrative domain network verification method, comprising: converting router configuration files of respective application servers into input formats determined by a secure multi-party computation protocol to ensure compatibility between the respective application servers; performing simulation processing on the router configuration files meeting the input formats to enable the multi-administrative domain network to generate a secret data plane after convergence; and performing data plane verification on the secret data plane to obtain verification results about respective attributes of the multi-administrative domain network. The present disclosure also provides a multi-administrative domain network verification system.

This specification provides a method and apparatus for generating multiplicative quintuples. Multiple parties engage in a first data interaction based on secure multi-party computation, enabling each party to obtain a first secret share of the triples within a specified domain space. Next, through a second data interaction based on secure multi-party computation, for each element in the triples, based on the first secret share held by each party, samples are taken from multiple equivalence classes within the specified domain space. This results in each party obtaining an adopted secret share, which serves as a second secret share for the remaining elements in the quintuple to be generated. The second secret share and the first secret share constitute the secret share of the quintuple to be generated.

A computer-implemented method for providing correlated randomness for secure multiparty computation using a plurality of computing machines. The method includes: executing a trusted execution environment system; setting up at least one correlated randomness generator in the trusted execution environment system; remote attestation of a single correlated randomness generator by all computing machines or remote attestation of a plurality of correlated randomness generators by in each case one computing machine, injecting in each case at least one secret by all computing machines into a single correlated randomness generator or injecting at least one secret by one computing machine into in each case one correlated randomness generator; checking the injected secrets by the correlated randomness generator(s); generating correlated randomness for the computing machines depending on the injected secrets; distributing the correlated randomness to the computing machines, wherein each computing machine receives only the correlated randomness created for it.

The application provides a transformer holographic intelligent test method and system, belongs to the field of transformer testing and state evaluation, and is used for solving the problems of test system module fragmentation, information island, low data reliability and low scheduling efficiency in the related art. The application unifies the representation of test resource state, transformer physical behavior, safety risk and health degradation trend by constructing a physical information neural random field global model; realizes model collaborative updating under multi-station data privacy protection by using a federal security multi-party computing protocol; realizes trusted evidence preservation of key states and token economy closed loop by using a trusted execution environment and a directed acyclic graph-based distributed ledger; realizes adaptive optimization of resource scheduling by coupling Bayesian optimization and token price dynamics. The application can significantly improve the intelligent level, data reliability and resource utilization efficiency of transformer testing.

Systems and methods for validating analytics or machine learning models using secure multi-party computation (SMPC) are disclosed. The system operates with a data owner party providing a validation data set and an analytic owner party supplying a specification of the analytic or machine learning model. The analytic owner party converts the specification into an oblivious computation and compiles it into a circuit of operations compatible with an SMPC protocol. The data owner party downloads the compiled specification and constructs a validation circuit to execute the model against the validation data set without revealing the data or model details. The results of the SMPC are evaluated to produce performance statistics of the model, ensuring privacy for both parties' data throughout the process.

The application discloses a kind of information security evaluation method and system based on cloud computing, it is related to information security technical field, including, acquisition multi-modal data, adopt node identifier to generate space-time data package by encryption;Through edge node, threat characteristics are aggregated using secure multiparty computation, construct 58-dimensional square matrix, construct risk assessment model based on quantum heuristic algorithm, apply Do-Calculus algorithm to locate root cause threat, federal reinforcement learning dynamically optimizes model weight, output threat vector to cloud causal engine;Combine threat intelligence to construct space-time causal diagram, apply Do-Calculus algorithm to locate root cause threat, generate dynamic defense strategy, KL divergence constraint optimization access control, and store strategy execution log in local encryption, generate evaluation audit log.The method disclosed in the application solves the problem of insufficient correlation of cloud computing multi-modal data, defense lag and verification fault, and realizes the coordinated breakthrough of evaluation accuracy and response efficiency.

Systems and methods for implementing a secure multiparty protocol for fine-tuning of language models are disclosed. An end-to-end privacy-preserving protocol using secure multi-party computation (MPC) and executed on a plurality of computing nodes enables fine-tuning a language model targeting classification tasks using private, sensitive data while providing secure protection of the training data and without sacrificing model accuracy.