575 results about "Secure computing" patented technology

The invention discloses a collaborative sharing method, system and device on an energy data chain fusing multi-party security calculation and zero knowledge proof and a storage medium, and belongs to the field of energy data security sharing and block chain privacy protection, and the method comprises the steps: executing desensitization processing on original data according to types, and generating a cryptographic commitment value and a data hash identifier; encrypting and fragmenting the desensitized data, registering a task structure body through an intelligent contract, and uploading the task structure body; and each node decrypts the ciphertext, executes calculation and generates a zero-knowledge proof, and submits the zero-knowledge proof to the block chain to be verified by the smart contract. According to the method, data privacy protection and result verifiability unification are realized, cross-mechanism cooperation efficiency is improved, an auditing system on a whole process chain is constructed, and safe sharing of energy data is guaranteed.

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

The invention relates to the technical field of traffic data processing, and discloses a traffic edge computing data security sharing method and system, and the method comprises the steps: collecting traffic edge computing network multi-source heterogeneous data, and carrying out the classification standardization processing to generate a structured data set; designing a dynamic data sharing security protocol based on a multi-party security computing protocol and a homomorphic encryption algorithm; verifying the authority of a requester in a multi-level manner by using an attribute-based access control model and a zero-knowledge proof mechanism, and generating a dynamic access token; storing data by adopting a fragmentation storage and redundancy encryption strategy, and recording storage information through a hash chain; and dynamically adjusting the encryption strength and the sharing strategy according to the network threat level and the data sensitivity. The method effectively guarantees safe sharing of traffic data, accurately controls access authority, improves storage and sharing efficiency, adapts to complex network environment changes, and provides powerful support for development of an intelligent traffic system.

The invention discloses a data processing method based on computer software development, which belongs to the technical field of data processing, and comprises the following steps: S1, constructing an adaptive analysis engine driven by a knowledge graph, and carrying out multi-modal data semantic modeling and context labeling by adopting a multi-modal data feature fusion technology; and S2, designing a cross-node distributed data cleaning and dynamic fragmentation optimization strategy based on a differential privacy and feature space alignment technology, and through hierarchical deployment of a national cryptographic algorithm and homomorphic encryption, realizing data full life cycle security protection, ensuring data asset security by static storage encryption, and supporting security calculation requirements by ciphertext operation; attribute-based encryption fine-grained access control accurately matches a data use permission, and a block chain technology ensures traceability and tamper-proofing of data operation; the problem that a traditional encryption scheme is insufficient in flexibility is solved while the compliance requirement is met, and a trusted infrastructure is established for cross-domain data sharing.

The invention discloses a market supervision data exchange platform migration and security enhancement method and system based on a credential environment, and relates to the technical field of network security. In the data migration process, the consistency and privacy protection of the data are ensured through Hash check and a multi-party security computing technology; a domestic commercial cryptographic algorithm and a quantum resistance encryption technology are introduced, so that the protection capability of the platform to existing security threats is effectively improved, and the resistance to future quantum computing threats is enhanced; an intrusion detection system and a virtual honeypot node are integrated, and a defense strategy is monitored and updated in real time in combination with a deep learning model, so that novel attack behaviors can be detected and coped with in real time; according to an intrusion detection alarm and a block chain audit result, a security policy and resource allocation are dynamically adjusted in combination with a machine learning technology, it is ensured that high-risk nodes are monitored in time, resource allocation is automatically optimized, and the resource utilization efficiency and security of the system are improved.

The invention discloses an electronic seal management and control method and system, and belongs to the technical field of electronic seal management, and the method comprises the steps: carrying out the identity verification of a seal use request; dividing a private key of the electronic seal into a plurality of key shares; a digital signature of the electronic seal is generated through cooperative calculation of a threshold signature mechanism; managing electronic seal key access in a trusted execution environment, and executing global signature calculation in a hardware encryption module; adopting a zero-knowledge proof algorithm to generate verifiable calculation proof data in the digital signature calculation process; and establishing a quantum communication link and generating a transmission key, and managing key transmission. According to the invention, the distributed storage private key is calculated through multi-party security, so that the risk of single-point leakage is eliminated; zero-knowledge proof is adopted to ensure that signature calculation is verifiable, and signature credibility is improved; secret key access is dynamically managed and controlled based on the trusted execution environment, and safety and flexibility are enhanced; quantum key distribution is utilized to ensure key transmission safety, and quantum attacks are resisted.

The invention discloses a data security method and system based on privacy computing and multifunctional encryption, and relates to the technical field of privacy security computing. Verifying the operation authority of the computing node on the encrypted data block through a zero-knowledge proof protocol, and generating a strategy compliance proof; a differential privacy budget real-time verification mechanism is adopted to verify the privacy protection intensity of strategy compliance proof, and a temporary permission token which passes authorization is obtained; based on the temporary permission token, performing encryption calculation allowed by strategy compliance proof on the encrypted data block through homomorphic operation, and generating an encryption calculation result and a calculation correctness proof; carrying out joint cryptographic packaging on an encryption calculation result, a strategy compliance proof and a calculation correctness proof, and outputting security data through a verifiable calculation protocol; according to the invention, through a differential privacy budget real-time verification mechanism and homomorphic and cryptomorphic calculation based on the temporary permission token, dual guarantee of privacy protection and data security is realized.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention discloses an adaptive privacy security calculation method and system based on medical data feature perception, and relates to the technical field of data privacy protection. The method comprises the steps that a plurality of participating nodes extract structure and semantic features of local medical data, and data feature description information is generated; operating environment parameters and privacy compliance indexes are collected to form node environment state information; the coordination node generates privacy protection strategy configuration based on the information, and determines disturbance intensity, an encryption mode, a data processing permission and a cooperative computing mode; performing differential privacy processing by the participating nodes, generating an intermediate calculation result and submitting the intermediate calculation result to the coordinating node; and the coordination node executes joint aggregation calculation, outputs a target result, and performs privacy risk assessment and strategy dynamic adjustment. The medical data privacy protection method effectively achieves differentiation, controllability and adaptivity of medical data privacy protection, is suitable for various calculation tasks such as federated learning and statistical modeling, and has high practicability and safety.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

Provided in the present application are a server, a terminal and a security system. The server comprises an authentication and key management module, a secure multi-party computation engine and a secure-channel management module, wherein the authentication and key management module is used for performing identity verification on a terminal, generating a first private key corresponding to the terminal and a first public key corresponding to the first private key, and generating a first session key on the basis of a second public key of the terminal and the first private key; the secure multi-party computation engine is used for verifying fingerprint information of the terminal and cooperating with a plurality of terminals to perform identity verification on a server; and the secure-channel management module is used for establishing a secure channel with the terminal, encrypting communication data on the basis of the first session key, and using the secure channel to send the encrypted communication data to the terminal. The solution of the present application can effectively solve the security problem of communication between a terminal and a server, and has the advantages of high flexibility and a low cost.

The invention relates to the field of smart city management and information security, and provides a federated learning CIM system information security protection method based on a block chain and a TEE, and the method achieves the security sharing and cooperative computing of multi-party data through combining the data tamper resistance of the block chain technology and the security computing capability of the TEE, and improves the security of the multi-party data. And the data privacy and the security of the model training process are ensured. According to the core technical scheme, the method comprises the steps of deploying an intelligent contract in a block chain network, and managing data access permission; local model training and security aggregation are carried out in a trusted execution environment, and global model parameters are protected through a differential privacy technology. The method can be used for traffic management, environment monitoring, energy optimization and other scenes in a smart city, and provides efficient, safe and intelligent decision support for a city management system.

The invention discloses an AI large model security all-in-one machine and a security channel establishment method and device, and relates to the technical field of multi-party security computing. The AI large model security all-in-one machine comprises a security processor and a physical memory module; the security all-in-one machine is provided with a plurality of trusted execution environments (TEE), different TEEs have different secret keys used for memory encryption and decryption and serve as memory secret keys, the memory secret keys of the TEEs are generated by a memory encryption module of a security processor based on a hardware trusted root of the security processor, and different memory spaces which are independently used are distributed to different TEEs on a physical memory module; an AI large model application is installed on each TEE, and data generated by the AI large model application is encrypted, written and decrypted and read in the memory space allocated for the TEE by the memory security module by using the memory key corresponding to the TEE. By adopting the scheme, the data security for localized deployment of the AI large model is improved.

The invention provides a medical data sharing method and system based on multi-party security computing, and the method comprises the steps: carrying out the credible access control of participants through zero-knowledge proof and biological feature authentication, and enabling the participants to comprise a medical institution, a medicine enterprise / research institution, a patient and a supervision institution; after credible access control is passed, when a medical institution extracts patient data, k-anonymity, differential privacy and homomorphic encryption technologies are utilized to encrypt and desensitize the extracted patient data and store the data to a block chain; receiving a calculation task request and calculation parameters published on the block chain by a medicine enterprise / research institution, realizing mixed circuit and secret sharing mixed multi-party security calculation by a calculation node based on an ABY3 framework, and verifying the compliance of a calculation result; and receiving a result verification and auditing request of the supervision mechanism, and if the calculation result is verified to be within an authorization range through the smart contract, allocating rewards to the medical mechanism, the calculation node and the block chain platform according to a preset proportion, thereby effectively improving the security and timeliness of medical data sharing.

The invention relates to the field of new energy power systems, and discloses a multi-dimensional index-based new energy base network construction capability evaluation method, which comprises the following steps of: constructing five-dimensional dynamic tensor fusion multi-source data, optimizing low-rank decomposition by combining a quantum annealing algorithm, and embedding a power grid topology hole feature constraint; adopting a federated learning framework to realize distributed parameter optimization, and introducing a quantum noise gradient to eliminate resolution difference; based on a causal graph model and back door adjustment stripping hybrid bias, generating an unbiased evaluation index; and dynamically correcting quantum annealing parameters through a closed-loop feedback mechanism. According to the method, multi-dimensional data fusion, topological vulnerability perception, privacy security calculation, unbiased evaluation and adaptive optimization are realized, and the precision and reliability of new energy base networking capability evaluation are improved.

The present invention relates to the field of privacy computation. Disclosed are a secure multi-party computation method and system based on a decision tree and privacy protection. In a data processing stage, the method uses a gradient-based one-sided sampling algorithm, so as to exclude most small-gradient samples, thus ensuring balance in accuracy while reducing the data volume; then, the method uses a histogram algorithm to construct a decision tree, thus reducing the memory consumption and increasing the computational speed; and in addition, the method also uses a differential privacy technology to perform encryption and perturbation processing on parameters of local models, so as to ensure that individual privacy information is not leaked. The secure multi-party computation method based on a decision tree and privacy protection of the prevent invention can implement prediction tasks such as classification efficiently and reliably while protecting privacy.

The invention discloses a customer privacy protection method based on homomorphic encryption and data desensitization, and particularly relates to the technical field of data privacy protection. Original customer data are collected, data cleaning, standardization and sensitive information identification and classification are carried out, the identified customer sensitive information is dynamically calculated, and differential privacy noise parameters are applied, so that the customer privacy protection is realized. The method comprises the following steps: generating dynamic desensitization data, encrypting key numerical sensitive information in the generated dynamic desensitization data, generating ciphertext data, and constructing a security calculation and analysis platform which comprises a ciphertext calculation engine and a security query interface and is used for executing ciphertext addition and multiplication operations on the generated ciphertext data to form an encryption analysis result. Authorization decryption and result delivery are carried out on the encrypted analysis result, and the privacy protection effect and the data use condition of the whole process are recorded in an audit log, so that traceable privacy protection closed-loop management is realized.

According to the electric bicycle data collaborative sharing method based on multi-party security computing, the collaborative computing system is constructed, the collaborative control nodes in the collaborative computing system are independent of the business departments, and the collaborative computing system allocates at least one computing execution node to each business department. The calculation execution nodes are in communication connection with a database of a business department, and each calculation execution node is in communication connection with the coordination control node; the method comprises the following steps: splitting a calculation task in a collaborative governance service related to cross-department data sharing based on service logic, and obtaining a single-department calculation sub-task and a cross-department calculation sub-task after splitting; each calculation execution node executes a calculation task which only relates to the data of the business department, and only submits a calculation result to the coordination control node; all calculation tasks related to cross-department data are completed at the coordination control node; and then the coordination control node feeds back the data requested by the business department to the requesting department.

The invention provides a full-link detection and trusted industrial control method and system based on a domestic chip, and relates to the technical field of industrial automation and information security. The system comprises a security calculation module which is constructed by integrating a TPM (Trusted Platform Module) in a domestic chip; the special hardware encryption acceleration module is designed based on an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array) architecture and is integrated in a domestic chip; the trusted execution environment module is constructed based on a security isolation technology; the anomaly detection module is constructed based on a pulse neural network; the block chain auditing module is constructed based on a block chain framework; the dynamic security policy engine is used to automatically trigger a security response policy. The invention constructs an integrated industrial safety control system based on a domestic chip, and the system is especially suitable for intelligent manufacturing and high-safety-level industrial scenes. The problems that in the prior art, full-link safe starting cannot be achieved in a localized chip environment, an efficient encryption and trusted execution environment is lacked, and the real-time anomaly detection and log auditing capacity is insufficient are solved.

The invention discloses a cross-subject data security sharing system and method based on multi-party security computing, and relates to the technical field of information security, and the method comprises the steps: constructing a trust verification network based on a block chain, deploying a smart contract, setting an access permission rule for each record in a security data record set by using an autonomous access control method, and carrying out the security sharing of the records in the security data record set; the smart contract performs legality verification on the access request according to permission rules in a data authorization rule base, and obtains an access permission response containing an authorization token; and performing multi-party security calculation according to the access permission response, recording a calculation result into a block chain log, and performing data security sharing. According to the invention, by constructing the trust verification network based on the block chain and deploying the smart contract, the establishment of a decentralized trust mechanism is realized, the security and reliability in a multi-party cooperation environment are ensured, and the beneficial effect of enhancing the single-point failure risk resistance is achieved.

The invention relates to a private data sharing method based on a block chain and privacy security computing, and relates to the technical field of block chains, a data owner performs local encryption processing on to-be-shared private data, and generates encrypted data and identification information in combination with data desensitization and format standardization preprocessing operation; a smart contract is deployed through a block chain network, an abstract of encrypted data, an access control strategy and data index metadata are written into a block chain, a data demander initiates a joint calculation request through the smart contract according to a data index on the block chain and a demand of the data demander, identity verification and permission auditing are carried out, and after permission auditing is passed, the data demander sends the joint calculation request to the block chain. And performing conjoint analysis on the encrypted data, after the analysis is completed, automatically recording a calculation result abstract by the smart contract, safely returning a joint calculation result to a data demander, triggering a data traceability and automatic destruction mechanism according to the smart contract, and automatically clearing all intermediate data, keys and encrypted fragments.

The invention relates to the technical field of confidential computing, and discloses a data credibility-oriented vehicle-mounted integrated security computing system and credibility construction method, which comprises the following steps of: establishing a static trust chain by using a hardware trust root, shielding external interruption in a credible execution environment, and controlling a performance monitoring unit; synchronously collecting real-time hardware micro-architecture events of the key business algorithm to generate a runtime feature vector; according to the method, the instruction stream during operation is anchored by utilizing the physical characteristics of the micro-architecture, the hijacking attack of the control stream is accurately identified in a ciphertext environment, the hijacking attack of the control stream is accurately identified, the hijacking attack of the control stream is accurately identified, the hijacking attack is accurately identified, and the hijacking attack of the control stream is accurately identified. Strong coupling verification of a calculation result and an execution behavior is realized, and logic safety of a vehicle-mounted platform is ensured.

The invention relates to the technical field of security computing, and discloses a privacy statistical method and system based on multi-party security computing and trusted execution environment collaboration.The method comprises the steps that threat intelligence statistical values collected by all security mechanisms are fragmented, and secret fragmentation values are obtained; calculating an enclave local certification structure according to the enclave environment safety parameters of the statistical nodes, and performing signature verification based on the enclave local certification structure to obtain a verification report; generating a session key based on the verification report, and performing encrypted transmission on the secret fragment value based on the session key to obtain an encrypted secret fragment; and reconstructing the encrypted secret fragment to obtain the threat intelligence statistical value, and carrying out multi-dimensional statistics on the threat intelligence statistical value to obtain a threat intelligence statistical result. According to the method and the system, multiple security assurance of threat intelligence privacy statistics is realized through a secret fragment and security enclave remote certification cooperation mechanism.

The invention relates to the technical field of computers, discloses an astronomical sensitive data security processing method, device and system based on privacy calculation, and a storage medium, and aims to solve the problems that in astronomical data cross-mechanism joint analysis, original data is easy to leak, the cooperation efficiency is low, and a traditional desensitization or encryption method is difficult to consider security and availability at the same time. The method specifically comprises the following steps: each participant deploys a private computing agent node locally, and original data is not out of a domain; the task coordination center issues an analysis task; the proxy node extracts and preprocesses local data, and loads a corresponding secure multi-party computing protocol template; according to the method, share segmentation is carried out on multi-modal data such as images, spectrums and star catalogues by adopting addition homomorphic secret sharing, and distribution is carried out through a national secret SM4 encryption channel; and multiple parties cooperatively execute task-oriented security calculation in an encrypted state, and the result is aggregated and returned with the minimum information amount. The method has the effect of realizing high-precision safe collaborative analysis which is available and invisible.

The present disclosure relates to techniques for automated and adaptive cloud security management. Embodiments provide for, at an electronic device configured to interface with a cloud computing environment, initiating one or more transactions in the cloud computing environment using a first identifier to cause a first service of the cloud computing environment to generate a first set of data including the first identifier and a second identifier, and a second service of the cloud computing environment to generate a second set of data including a third identifier and a fourth identifier. Embodiments also provide for automatically determining whether the first identifier corresponds to the third identifier, and, in accordance with a determination that the first identifier corresponds to the third identifier, associating the second identifier and the fourth identifier to generate a linkage between the first and second services.

The invention discloses a data privacy protection method based on multi-party security computing and a block chain, and the method comprises the following steps: 1, creating a multi-party security computing task request, and distributing the multi-party security computing task request to each participant node; 2, performing Kronecker perturbation coding on local original data, and performing Hash processing through an SHA-256 Hash algorithm to generate a perturbation Hash value; 3, recording access behaviors, and generating an access behavior track sequence; step 4, calculating a Soft-DTW distance between the access behavior track sequence and the historical access track sequence; 5, constructing an adjacent matrix and a Laplacian matrix, and carrying out eigenvalue decomposition; step 6, calculating the Euclidean distance between the access embedding vector and the legal access embedding vector; and 7, generating a result abstract and writing the result abstract into a task recording module. According to the method, the Kronecker perturbation coding and the Hash algorithm are combined, so that the data privacy protection, the calculation transparency and the compliance verification capability are improved.

The invention provides a sensitive data collaborative auditing platform and method based on multi-party network security computing, and relates to the technical field of multi-party security computing. The platform comprises a data input layer which is used for each data holder to acquire original data, standardize the original data, perform differential privacy processing on the standardized data, encrypt private data by adopting a Paillier homomorphic encryption algorithm to obtain a ciphertext, segment the ciphertext by adopting Shamir secret sharing to obtain a plurality of segment ciphertexts, uploading the fragmented ciphertext to a confusion layer; the confusion calculation layer is used for aggregating the fragmented ciphertexts to obtain aggregated ciphertexts, fragmenting the aggregated ciphertexts by adopting Shamir secret sharing to obtain a plurality of fragmented aggregated ciphertexts, and distributing the fragmented aggregated ciphertexts to audit layer nodes; performing target calculation on the fragmented aggregation ciphertext by adopting MPC calculation logic; and the auditing execution layer is used for converting the auditing rule into MPC computational logic. According to the application, the security, efficiency and cross-domain compatibility of multi-party collaborative auditing can be improved.

Processing a payment transaction from a payer (operating a payer computing system) to a payee (operating a merchant computing system) by a secure computing system. The secure computing system outputs a financial account registration request form to the payer computing system (e.g., within a window or frame that is displayed within an ecommerce webpage provided by the merchant computing system) for the payer to provide sensitive financial account information, securely stores the sensitive financial account information, maintains compliance with an information security standard (e.g., a Payment Card Industry Data Security Standard), and provides a non-sensitive electronic data token representing the sensitive financial account information to the merchant computing system. The merchant computing system can then process the payment transaction using the sensitive financial account information represented by the non-sensitive electronic data token without actually receiving—and, therefore, having to secure—the underlying sensitive financial account information.

The invention discloses a cloud edge collaborative information interaction processing device, method and system with an endogenous security architecture. The equipment comprises a main processor, a hardware security chip, a programmable logic unit, a communication unit and an equipment management unit. A trusted execution environment is constructed in the hardware security chip and is in isolated communication with a common execution environment of the main processor through a security bus, a security computing engine, a remote attestation module and a trusted key warehouse are arranged in the trusted execution environment, and the programmable logic unit is directly controlled by the trusted execution environment to realize dynamic hardware acceleration. The communication unit is provided with independent interfaces which are connected with the two execution environments respectively, unification of hardware-level security isolation and efficient cooperative computing is achieved, and the problems of privacy disclosure and untrusted process in cloud edge collaboration are solved.