2910 results about "Homomorphic encryption" 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.

The invention relates to the technical field of intelligent decision making, in particular to a simulation system intelligent decision making method and system based on a knowledge graph and federated learning, and the method comprises the steps that each simulation node constructs a knowledge graph sub-graph based on local dynamic data, and multi-modal semantic representation is generated through space-time modeling and event chain reasoning; the federal center initializes a simulation decision model architecture and issues the simulation decision model architecture to each node; each node uses a local knowledge graph to train a time sequence diagram network, extracts an equipment degradation path and abnormal propagation characteristics, and uploads gradient parameters in combination with homomorphic encryption; the federation center fuses the multi-node model through a dynamic weight aggregation algorithm to generate a global decision model; and driving knowledge graph evolution based on real-time data, and performing causal reasoning and decision optimization on an event chain through a federal model. According to the method, the problem of insufficient dynamic decision adaptability in a multi-source data island and privacy sensitive scene in a simulation system is solved.

The invention relates to the technical field of judicial data security, and discloses a judicial system confidential data security circulation method based on a block chain technology. Collecting multi-source judicial data, identifying sensitive information through a large language model, and performing differential privacy desensitization processing; constructing an SM4 encryption and TLS 1.3 end-to-end secure channel, generating a data hash fingerprint, and writing the data hash fingerprint into an alliance chain evidence based on a PBFT consensus mechanism; designing a multi-modal classification engine to perform feature extraction and intelligent classification; establishing a hybrid authority model to integrate an XACML strategy and a Kafka queue, and implementing dynamic authority control in combination with an RBAC / ABAC mechanism; a hierarchical encryption storage architecture is constructed, and homomorphic encryption retrieval and erasure code distributed storage are adopted; constructing a judicial knowledge graph based on a BERT model; and deploying a block chain auditing system, and combining LSTM anomaly detection and a DREAD risk assessment model to form a closed-loop risk control system. According to the method, the problems of security risk and privacy disclosure in judicial data cross-department circulation are solved.

The invention discloses a medical data security sharing method and system based on a block chain, and relates to the field of data security, and the method comprises the steps: constructing a medical data sharing block chain system; encrypting the medical data of the user by adopting a fully homomorphic encryption algorithm; organizing the encrypted medical data by adopting a Merkle tree structure, encrypting and storing the medical data in the Merkle tree through an IPFS distributed storage system provided by a third-party service node, and storing the root hash of the Merkle tree and the encrypted IPFS address of the medical data as index information of the medical data in a block chain through a medical data uplink smart contract; calling a privacy protection smart contract to verify the identity and authority of the requester by using a zero-knowledge proof method; according to index information of the request data, obtaining corresponding Merkle tree root hash and an encrypted IPFS address of the medical data, and returning the Merkle tree root hash and the encrypted IPFS address to the requester; in view of insufficient privacy protection in a medical data sharing process in the prior art, the security of privacy data is improved.

An AI-enhanced distributed system for neural network-based data compression leverages reinforcement learning optimization and privacy-preserving computation across edge and central computing devices to autonomously optimize efficiency and quality. The system includes a lightweight compression subsystem at edge devices that applies privacy-preserving preprocessing and partially compresses input data before securely transmitting it to central computing devices. A reinforcement learning agent continuously monitors system performance and automatically optimizes compression parameters, model selection, and task allocation based on multi-objective rewards. The central compression subsystem processes data using AI-optimized parameters and temporal modeling components. The system incorporates hardware detection capabilities that automatically select optimal compression models based on available processing resources and implements homomorphic encryption for computation on encrypted data while coordinating federated learning across distributed devices. This AI-enhanced distributed approach improves bandwidth efficiency, energy consumption, and adaptability while ensuring data privacy and security.

The invention discloses a risk control credit monitoring method based on cloud computing, and belongs to the technical field of cloud computing, and the risk control credit monitoring method based on cloud computing comprises the following steps: S1, collecting user transaction data and behavior track data in real time; s2, cleaning and standardizing the data; s3, constructing a multi-dimensional risk assessment model based on machine learning; s4, dynamically generating a credit score according to the risk characteristics; s5, triggering an early warning mechanism for abnormal transactions in real time; and S6, generating a visual risk control report and updating a monitoring strategy. According to the method, multi-source heterogeneous data are integrated through federated learning, hierarchical privacy protection is realized in combination with homomorphic encryption and differential privacy, risk assessment real-time performance is improved by using a hybrid cloud resource scheduling and dynamic model updating technology, and a compliance audit closed loop is constructed based on a block chain and interpretability analysis.

The invention discloses an artificial intelligence data privacy protection system based on a block chain and federated learning, and relates to the technical field of block chains and federated learning, and the system comprises a block chain module which employs a main chain-side chain double-chain architecture, a main chain stores a global model hash value and node reputation evaluation data, and a side chain module is used for storing node reputation evaluation data; the side chain stores the encrypted local model parameters through a fragmentation technology; the federated learning module comprises a dynamic difference privacy algorithm and a gradient ternary processing unit, and is used for adding noise to the gradient in a local training stage and converting the gradient into a ternary numerical format; the privacy protection module is used for integrating homomorphic encryption and zero-knowledge proof technologies and realizing ciphertext aggregation and verification of model parameters; and the malicious node detection module is used for identifying abnormal gradient update based on cosine similarity and Multi-Krum algorithm, and is linked with node reputation data in the block chain module. Through a system architecture and a privacy protection mechanism, the efficiency and performance of federal learning are improved while data privacy is ensured, and the method has a wide application prospect.

The invention provides a service data security protection method and system for digital enterprise management, and the method comprises the steps: obtaining an access request sequence initiated by a target user at a service operation terminal, generating a dynamic access control strategy vector according to a historical behavior track associated with an access operation identifier, and carrying out the dynamic access control strategy vector; analyzing the dynamic access control strategy vector through a strategy decoder, and generating a real-time access permission instruction; based on the real-time access permission instruction, performing homomorphic encryption mapping on a sensitive data segment in the request context information to generate a ciphertext transmission channel, and performing security parameter synchronization on the ciphertext transmission channel and the cross-department conjoint analysis model; and calling a federated learning framework to carry out multi-modal feature fusion on the real-time operation flow of the service operation terminal, generating an abnormal operation confidence score, triggering a cross-level interception protocol when the abnormal operation confidence score exceeds a dynamic threshold, and rolling back the current session state to a security baseline version. According to the invention, the accuracy and response timeliness of anomaly detection can be improved.

The invention belongs to the field of intelligent edge systems and privacy enhancement computing, and particularly relates to a privacy protection-oriented robot large model cloud edge end collaborative reasoning and federated learning system, which comprises a cloud server layer used for deploying a large-scale pre-training model and executing complex reasoning and global federated learning coordination; the edge calculation layer is used for deploying an intermediate layer model and executing local data aggregation, privacy protection processing and intermediate feature calculation; the terminal equipment layer is used for deploying a lightweight model and executing data acquisition, primary processing and lightweight reasoning; the federated learning framework is used for optimizing the model; the privacy protection module is used for integrating data localization, differential privacy, homomorphic encryption, secure multi-party computing and a block chain verification mechanism; the adaptive allocation module is used for dynamically adjusting computing resources. According to the method, the problems of privacy leakage risk, computing resource limitation, network delay, insufficient data isolation and the like of the traditional AI service in a robot scene are solved, and efficient privacy protection and data security isolation are realized.

A distributed intelligent authentication method based on dynamic multi-modal fusion relates to the field of network security, and adopts an alliance chain + DAG hybrid block chain architecture, combines a threshold signature to realize secret key fragment management, and switches among PBFT, Raft and probabilistic algorithms through a dynamic consensus mechanism to improve authentication efficiency. The multi-mode authentication module is based on a dynamic weight distribution algorithm, integrates biological characteristics, behavior analysis, equipment fingerprints and environmental factors, and combines an LSTM-GAN model and a quantum random number driven challenge-response mechanism to realize zero-trust verification under environmental perception. The session management module generates a session key by using a chaotic mapping algorithm. In the aspect of privacy protection, CKKS homomorphic encryption, zero-knowledge proof and attribute-based encryption are fused. According to the method, the block chain technology, the secure multi-party computing technology, the machine learning technology and the quantum cryptography technology are fused, and a high-performance, high-security and strong-privacy-protection distributed authentication solution is provided.

The invention discloses a data security and privacy management method and system based on a block chain technology, and the method comprises the following steps: S1, collecting original data in a target system, and carrying out the preprocessing of the original data; s2, constructing an access control strategy set; s3, converting into a structured strategy configuration file, and deploying a smart contract; s4, matching and judging the access request and a preset strategy by the intelligent contract; s5, judging whether the access request is legal or not; s6, performing structured processing on the access log, and writing the access log into a block chain account book; s7, intelligently selecting an encryption mechanism, and providing access return data to the access party; and S8, carrying out modeling prediction on the access mode by adopting a federal gradient lifting tree algorithm. The block chain intelligent contract and the federal gradient boosting tree algorithm are fused, differential privacy, zero-knowledge proof and homomorphic encryption technologies are combined, data intelligence and refined privacy protection are realized, and the method has the advantages of high credibility, high intelligence and high traceability.

The invention discloses a distributed storage method and system for data security, and belongs to the technical field of data storage security, the method comprises the following steps: firstly, fragmenting original data, and generating a unique identifier for each data fragment; for each fragment, an encryption algorithm and key credentials are adaptively selected, and the key credentials are stored in a key management system. By performing homomorphic encryption on the key voucher, an encryption handle is generated, and efficient positioning and retrieval of the key are realized. According to the method, a differentiated encryption strategy is adopted, a most suitable encryption scheme is selected according to fragment characteristics, and encrypted data fragments are dispersed and stored in a plurality of distributed storage nodes. A global mapping index is constructed in a metadata management server, and a corresponding relation between an original data global identifier and metadata of each data fragment is recorded, including information such as identifiers before and after encryption, a storage position, an encryption algorithm, a key handle and the like. According to the method, the data leakage risk is effectively reduced, and the safety and reliability of the storage system are improved.

The invention provides an energy storage power station battery health state diagnosis method and system based on big data, and relates to the technical field of energy storage batteries, and the method comprises the steps: collecting multi-mode operation data of an energy storage power station battery, carrying out homomorphic encryption to obtain ciphertext data, carrying out analysis and calculation, extracting explicit and implicit correlation features, and obtaining fusion time sequence data; based on fusion time sequence data, extracting multi-path quantum entanglement features, combining with a self-evolution quantum attention mechanism, dynamically adjusting the phase and amplitude of quantum bits, generating diagnosis feature enhancement representation, realizing entanglement storage through a quantum circuit, and combining with a gating unit to predict a health state sequence; and determining a battery health prediction result, and obtaining a battery health state diagnosis result by combining quantum state superposition and quantum parallelism, searching a diagnosis strategy combination space on line, embedding a multi-order causal relationship tensor model, modeling causal relationship strength between battery performance indexes, determining a battery health degradation index, generating a causal diagnosis map and obtaining the battery health state diagnosis result.

The invention relates to the technical field of agricultural product quality tracing, and discloses an agricultural product quality tracing system based on a block chain technology, and the system comprises a data collection module which is used for collecting environment data, quality detection data and position information of agricultural products in the links of planting, processing, transportation, storage and the like through Internet of Things equipment; the encryption module is used for performing preliminary symmetric encryption processing on the collected data and encrypting sensitive data through a homomorphic encryption algorithm; and the intelligent contract module is used for automatically executing data storage, authority control, zero-knowledge proof verification and cross-chain data management on the block chain. According to the method, the authenticity and security of data in each link are ensured through automatic execution of data storage and authority control by using a distributed account book and a tamper-resistant characteristic of the block chain and through an intelligent contract, and meanwhile, an I PFS distributed storage system is combined with the block chain, so that the integrity of the data is further ensured, and the security of the data is improved. Therefore, all parties can share and query the tracing information trustingly.

The invention relates to the technical field of encryption communication, in particular to a data encryption communication method and system applied to an intelligent cash register. The method comprises the following steps: collecting user touch data and equipment hardware fingerprints, and carrying out high-dimensional feature fusion coding to obtain a joint identity certificate; obtaining an original key negotiation protocol and transmission metadata of an opposite-end server, and executing multi-logic path mapping and channel encryption configuration to obtain distributed ciphertext channel parameters; encrypting the distributed ciphertext channel parameters and the corresponding transmission metadata, and then performing block content packaging and node hash calculation to obtain an audit chain hash block; performing homomorphic encryption processing on the obtained real-time transaction data to obtain a to-be-transmitted homomorphic ciphertext; and performing communication transmission anomaly detection on information transmission data acquired in real time, and triggering dynamic key re-negotiation so as to improve the communication feedback capability. According to the invention, the overall security and stability of data encryption communication between the cash register and the back-end server can be improved.

The invention discloses a multi-modal data real-time identification and cooperative processing system based on edge computing and federated learning. The multi-modal data real-time identification and cooperative processing system comprises a cloud center coordination node, a plurality of edge computing nodes, a cross-modal encryption engine, a federated learning controller and a model updating verification module. The cloud center coordination node executes federated learning model aggregation and dynamic task allocation, and generates a cross-modal encryption strategy; and the edge computing node is configured with a multi-modal data acquisition module, a local model training unit and a co-processing gateway to realize multi-modal data acquisition and local processing. The system encrypts vision, acoustics and text data by using differentiated algorithms such as spatial confusion, frequency domain permutation and homomorphic encryption; the federated learning controller carries out multi-modal feature fusion, hierarchical encryption and dynamic networking at the edge node; and the model updating verification module performs aggregation updating after ensuring parameter consistency by using secure multi-party calculation. According to the method, real-time processing and privacy protection of multi-modal data are realized, and the data co-processing efficiency is improved.

The invention provides a multi-private-domain visitor portrait sharing and privacy protection routing method based on federal learning, and relates to the technical field of data sharing and privacy protection, and the method comprises the steps: calculating the feature sensitivity and distributing a differential privacy budget, and adding mixed noise to the original data of a visitor portrait for privacy protection, according to the method, feature vectors are encrypted by adopting fully homomorphic encryption based on ideal lattices, threshold password segmentation is performed by utilizing a Viterbi algorithm, sub-key components are distributed to private domain environments, a hybrid network structure is constructed to extract features, federal optimization is performed based on data quality and historical contribution degree, and a feature extraction network is updated. The method comprises the following steps: carrying out feature dimension reduction by using a dual auto-encoder, extracting key features and generating user feature fingerprints, carrying out user grouping by calculating the similarity of the feature fingerprints, and realizing safe sharing of user similarity information and visitor portrait original data in combination with multi-party safe calculation and a hierarchical data access strategy.

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 relates to the technical field of data processing, and discloses an intelligent product page automatic generation method and system for the massive health industry. The method comprises the following steps: performing multi-modal analysis and augmented reality processing on a large health industry prototype graph and multi-source medical data to obtain augmented design information; performing dynamic knowledge graph mapping and causal reasoning on the enhanced design information to obtain semantic structure and knowledge associated data; performing adaptive micro-service deployment analysis on the semantic structure and knowledge associated data to obtain a containerized deployment strategy, and performing intention-driven API design and homomorphic encryption enhancement processing to obtain API interface specification data; performing component design and code generation processing on the API interface specification data to obtain a multi-modal interaction page code; and performing intelligent rendering and dynamic content filling on the multi-modal interaction page code to obtain a target product page. According to the method and the device, the efficiency and the accuracy of automatic generation of intelligent product pages in the large health industry are improved.

A computer-implemented method for performing at least one computational operation on an encrypted input by at least one processor of a server in a client-server setting, where parameters of the computational operation are private to the server and the input is private to the client is provided. The method includes receiving, by the server, a ciphertext c of a leveled homomorphic encryption (LHE) scheme as encrypted input. Randomness is homomorphically added by the server to the ciphertext c and the resulting ciphertext b is transmitted to the client. The server receives a refreshed ciphertext b′ obtained by the client in a ciphertext refresh procedure including decrypting and re-encrypting the ciphertext b. The server homomorphically removes the previously added randomness from the received refreshed ciphertext b′ to obtain a refreshed ciphertext c′. The server performs the at least one computational operation on the refreshed ciphertext c′.

The invention provides a privacy protection method and system based on big data security and privacy calculation, and relates to the technical field of data processing, and the method comprises the steps: obtaining original data feature extraction, obtaining data features, selecting a data desensitization algorithm to carry out data desensitization, carrying out encryption processing through a hybrid fully homomorphic encryption algorithm, and carrying out encryption processing through a hybrid fully homomorphic encryption algorithm. Homomorphic encrypted ciphertext data is generated and sent to the privacy protection computing platform; a data preprocessing protocol for privacy protection is constructed, data filtering and data dimension compression are carried out, multi-party security calculation is executed, a ciphertext calculation result is generated and returned to a data provider, verification is carried out, and if verification passes, decryption and filtering are carried out through a decryption algorithm to obtain a pure plaintext calculation analysis result; and constructing a reverse data desensitization model for reduction to obtain an analysis calculation result, constructing a dual-target optimization model for solving, obtaining an optimal balance point and generating an optimal decision result, desensitizing, uploading to a privacy protection block chain network, and performing distributed credible storage and sharing.

An FPGA-based accelerator for bootstrappable fully homomorphic encryption (FHE) employs (1) acceleration of scalar arithmetic operations using a multi-word approach for efficient utilization of standard-width components (multipliers / adders) on custom-width operands; (2) a performant, shift-based modular reduction technique that avoids the need for expensive multipliers; (3) an improved datapath for an expensive Key Switch operation; and (4) an efficient organization of on-chip memory for storing custom-width operands and supplying them at high bandwidth to computation units.

The invention relates to the technical field of agricultural data security, and discloses an agricultural data security sharing method based on federated learning, through cooperative operation of a user terminal module, a data classification module, a federated learning module, an agricultural cloud database and a security protection module, a user terminal encrypts, uploads, decrypts and feeds back agricultural data through an edge computing device; the data classification module performs multi-level label labeling on data of planting industry, animal husbandry and the like by using a differential privacy technology; the federated learning module is combined with homomorphic encryption to realize local model training and parameter aggregation; the agricultural cloud database stores encrypted knowledge assets by using an IPFS technology; the security protection module integrates block chain evidence storage and a dynamic access control mechanism, the workflow comprises data encryption uploading, privacy protection classification, federated model training, solution generation and security feedback, and by adopting a federated normal form of a data immobile model, the security protection module can effectively protect the security of the original data on the premise of ensuring that the original data is not out of the domain. And value mining and safe sharing of cross-regional agricultural data are realized.

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

The codebook-based homomorphic compression system is a novel approach that combines data compression and homomorphic encryption to enable efficient and secure computation on compressed data. It involves quantizing the input data, generating an optimized codebook using techniques like Huffman coding or deep learning, and compressing the data by replacing each value with its corresponding codeword. The compressed data is then encrypted using a homomorphic encryption scheme, allowing computations to be performed directly on the encrypted compressed data without decryption. The system includes a deep learning architecture trained on and operating on the homomorphically compressed and encrypted data, enabling inference and decision-making while maintaining data privacy. This approach combines the benefits of data compression, homomorphic encryption, and deep learning, enabling efficient storage, transmission, and secure computation on compressed data streams.

The invention provides an investment decision-making system and method based on federated learning and multi-modal risk control, and the system comprises a data collection layer which is used for integrating satellite remote sensing data, supply chain Internet of Things data and financial market data; the federal processing layer is used for constructing a distributed training framework based on a FedAvg algorithm and realizing cross-mechanism data fusion and global model parameter updating through homomorphic encryption and differential privacy; the intelligent decision-making layer adopts a TD3 reinforcement learning engine and adopts a dynamic gating mechanism to adjust fusion weights of texts, time sequences and image modalities in real time; and the execution monitoring layer is used for detecting transaction execution deviation in real time through a digital twinning technology, realizing abnormal response by adopting a CUSUM algorithm, and dynamically adjusting a federal learning rate through Bayesian optimization. The key technical defects of data islands, tail risk response delay, insufficient cross-market linkage analysis and the like of a traditional investment system are overcome through an innovatively designed federated learning architecture and a time-varying risk control model.

The invention provides a system platform for psychological assessment and emotion feedback. The system platform comprises a multi-source data acquisition module, an emotion data analysis module, a psychological state assessment module, an intervention scheme generation module, an emotion change feedback module, a scheme optimization module and a data security protection module. The emotion recognition accuracy is improved by synchronously acquiring physiological-behavior-environment data and combining with a dynamic feature fusion method, a personalized intervention strategy is generated based on a reinforcement learning algorithm, real-time feedback is realized, and a data security protection system is constructed by adopting federal learning and homomorphic encryption technologies, so that the data security is ensured.

The invention discloses an industrial equipment fault prediction system based on federated learning and a privacy protection method, and relates to the field of industrial equipment fault prediction. The data acquisition preprocessing module extracts fault features through compressed sensing downsampling, screens and uploads the fault features; the federal learning training module adopts a layered architecture and a dynamic algorithm to schedule a learning rate; the fault prediction and diagnosis module constructs a space-time diagram neural network and fuses a physical model to improve generalization; the privacy protection security communication module performs homomorphic encryption storage and zero-knowledge proof verification update; the knowledge graph construction reasoning module constructs a dynamic graph, locates a fault root cause through causal reasoning, and supports cross-device knowledge migration. By adopting the quantum and federated learning technology, the industrial equipment fault diagnosis accuracy is high, the attack resistance is high, the encryption efficiency is greatly improved, the model training time is shortened, cross-equipment knowledge migration is realized, the operation and maintenance cost is reduced, and the intelligent operation and maintenance development of the industrial equipment is promoted.

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 relates to a federated learning system based on multi-key homomorphic encryption and adaptive differential privacy, and belongs to the technical field of privacy computing. According to the system, on the premise that no trusted third party exists, a multi-client collaborative key generation and threshold decryption mechanism is achieved, it is ensured that model parameters are always in an encrypted state in the aggregation process, and leakage of a single node is prevented. By introducing a parameter sensitivity analysis and selective encryption strategy, the system only encrypts high-risk parameters, and the encryption burden is effectively reduced. Meanwhile, in combination with an adaptive privacy budget allocation mechanism, the system dynamically adjusts noise intensity according to a model training state, and model performance and convergence speed are maintained while privacy protection capability is improved. According to the method, high robustness and collusion resistance are realized, stable operation under the condition that part of clients are offline is supported, and the method is suitable for application scenes such as medical treatment and finance with high data sensitivity and strict performance requirements.