763 results about "Data privacy protection" patented technology

The present disclosure relates to a blockchain-based authentication system for electric vehicles and drones, and a method for authenticating electric vehicles or drones in a smart city. The proposed blockchain-based authentication system enhances security, privacy, and scalability for electric vehicles (EVs) and drones in smart city environments. It employs a consortium blockchain managed by city authorities and stakeholders, utilizing smart contracts for identity registration, credential issuance, and access control. Device nodes store cryptographic keys securely, while an optional authentication server facilitates off-chain integration. The authentication operation of the system includes, ensuring tamper-proof identity verification through on-chain validation. Authentication attempts are immutably recorded for auditing and anomaly detection. By eliminating single points of failure and strengthening data privacy, this decentralized authentication framework offers a scalable and secure solution for integrating EVs and drones into smart city infrastructures.

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 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 relates to the technical field of data privacy protection, and discloses a cross-platform social privacy collaborative protection system based on federated learning and a block chain. The system comprises a federal learning initialization module, a private data encryption module, a cross-platform data synchronization module, a block chain consensus verification module and an intelligent contract execution module. Global model initialization parameters are generated through a multi-party security aggregation algorithm, user data privacy is protected through hierarchical encryption, data are synchronized through a Hash time lock protocol and an intelligent contract, model updating is verified through an improved Byzantine fault-tolerant algorithm, and a privacy protection rule is triggered based on a differential privacy noise injection algorithm. The system effectively solves the problem of cross-platform social privacy protection, guarantees data security and privacy, improves federal learning reliability, optimizes data sharing and utilization, and is suitable for various cross-platform social scenes.

The invention discloses a financial data privacy protection system based on block chain security multi-party computing, and belongs to the technical field of data protection, and the system specifically comprises the steps that a data preprocessing module segments original financial data, and adds a unique watermark identifier; the distributed account book stores the Hash abstract and watermark mapping relation of the encrypted logic data fragment, and the digital signature and the timestamp are fused to ensure reliability; the computing node cluster receives data through a special interface, dynamically builds a computing group according to a service rule and outputs an encrypted computing result; bidirectional authentication is carried out on the zero-knowledge proof verifier and the computing node cluster to verify the data holding right; the encryption proxy gateway processes an output result of the computing node cluster to complete format conversion and ciphertext superposition; the cross-link routing module constructs a virtual coverage layer, cross-link transmission of encryption results is achieved through protocol conversion and three-way handshake verification, and a heterogeneous chain protocol converter and a verification assembly guarantee transmission safety; the financial data privacy security is comprehensively guaranteed, and the data processing efficiency and reliability are improved.

The invention discloses a driving vehicle road cloud collaborative safety management and control method and system based on trusted computing, and the method comprises the steps: generating an HMAC-SM3 data fingerprint through employing an SM3 Hash algorithm engine, and proposing an LSTM lightweight dynamic trust evaluation model based on a TinyML framework; through a step-by-step Hash verification and rollback mechanism, through Geohash coding and SM3 Hash joint calculation, based on an aggregation signature of a BLS12-381 curve and public key aggregation, the communication overhead during multi-RSU collaborative signature is reduced; distributed private key fragmentation decryption is realized by adding a homomorphic aggregation ciphertext; aggregating and delaying the compressed ciphertext; dynamically evaluating the credibility of the nodes by a Krum dynamic defense mechanism; the system comprises a vehicle side credible sensing layer, a roadside credible broadcast layer and a cloud credible decision-making layer. According to the invention, through layered credibility verification, dynamic trust chain extension and a data privacy protection mechanism, the security and credibility problems of multi-node cooperation in an automatic driving scene are solved.

The invention discloses a landslide prediction and early warning system and method based on a remote sensing technology, and is applied to the technical field of landslide early warning. Comprising a data acquisition module for acquiring multi-source data in a monitoring area; the cloud computing module is used for large-range landslide risk assessment and deformation monitoring; the edge calculation module is used for landslide risk assessment and deformation monitoring of a specific area; the end-cloud cooperative communication module is used for realizing data interaction between the cloud computing module and the edge computing module; the federal learning module is used for realizing cooperative training under data privacy protection of different areas; and the landslide early warning module is used for carrying out early warning based on landslide risk assessment and deformation monitoring results of the cloud computing module and the edge computing module. According to the invention, by using the deep learning model, the end-cloud collaborative architecture and the federal learning driven data fusion framework, efficient, low-cost and high-precision landslide risk prediction is realized.

The invention relates to the technical field of data processing and privacy protection, in particular to a data desensitization and integrity verification method and system based on a differential privacy algorithm, and aims to realize collaborative optimization of data privacy protection and integrity guarantee. Determining a total privacy budget by calculating global sensitivity, a data leakage risk coefficient and independent and joint data privacy information amount, dynamically allocating the privacy budget for a single-task or multi-task scene, and injecting noise based on an allocation result to realize differential privacy desensitization; meanwhile, a verification voucher is generated by using a hash function, a digital signature and an alliance chain, and the data integrity is ensured through signature verification, data consistency verification and block chain verification. The system comprises a data application processing module, a global sensitivity calculation module, a desensitization core processing module, a verification voucher generation module and an integrity verification module, is suitable for government affairs, enterprises and other scenes with high requirements for data privacy and integrity, and effectively improves the safety and reliability of data processing.

The invention provides a low-altitude industry chain information intelligent cooperation and airspace management platform based on federal learning. Through cluster ad hoc network and decentralized federated learning, the method is divided into three stages of data acquisition, data preprocessing and analysis application, low-altitude scene data communication of dynamic topology and static topology is realized, and the efficient communication requirement in a low-altitude operation scene is met. According to the system, differential data transmission strategies are adopted, optimization design is carried out on a single machine, a co-task cluster, a cooperative cluster and a non-cooperative cluster, and the communication efficiency and the resource utilization rate are improved. In the data preprocessing stage, federated learning aggregation of multi-source data fusion, data anonymization and decentralization is combined, and data privacy protection and safe sharing of three parties of industry, university and research are ensured. In the analysis and application stage, low-altitude aircraft task scheduling, airspace flow control and environmental condition evaluation are realized through aircraft analysis, airspace analysis, environmental analysis and digital twinborn technologies.

The invention discloses a self-service printing method based on an Internet of Things cloud platform, and the method comprises the following steps: user identity authentication and dynamic authorization: enabling a user to use a dynamic two-dimensional code or a one-time password to complete identity authentication and printing authority distribution through the Internet of Things cloud platform; lightweight cloudization of the printing task: user equipment fragments and compresses a file, and only uploads an encryption task description containing printing parameters to a cloud platform; cloud platform task scheduling: the cloud platform detects the state of the Internet of Things printing device in real time, and distributes the printing task to the optimal device according to the device load and the geographic position; distributed data transmission and edge analysis: the cloud platform transmits task fragments to an edge node through an Internet of Things protocol, and the edge device completes file recombination and analysis; by introducing a zero-knowledge encryption technology, edge calculation and dynamic task scheduling, the problems of insufficient data privacy protection, low efficiency, poor security, resource waste and the like in the prior art are solved.

The invention provides a data privacy protection method, which comprises the following steps of: acquiring an original data set, extracting a high-sensitivity field, calculating the correlation between the high-sensitivity field and other fields, and determining a high-correlation field combination; performing intelligent segmentation and scattered storage on the high-relevance field combination, and constructing a distributed hash table index; realizing encryption aggregation and asynchronous updating of global model parameters through a federated learning framework; constructing an adversarial sample for the high-relevance field combination, and training a privacy protection defense model; differential privacy perturbation processing is carried out on the high-sensitivity field; calculating an association risk index of the high association field combination in real time, and generating an association risk analysis report; dynamically adjusting a data segmentation strategy according to the associated risk analysis report, and optimizing local model training sample selection; and if the association risk index exceeds a preset security threshold, triggering adaptive updating of a differential privacy deassociation processing flow.

The invention discloses a network risk assessment model construction method and system based on an AI large model, and relates to the technical field of artificial intelligence and network security, and the method comprises the following steps: S1, a participating node splits a large model into a plurality of semantic independent fragmentation units through a dynamic fragmentation mechanism based on local multi-source heterogeneous data; according to the network risk assessment model construction method and system based on the AI large model, through a dynamic fragmentation federated distillation learning framework, the inherent contradiction between data privacy protection and AI large model training efficiency in cross-mechanism cooperation is effectively solved. A ten-billion-level parameter model is disassembled into semantic independent units by adopting a self-adaptive parameter fragmentation mechanism, the communication overhead is reduced on the premise of ensuring physical isolation of sensitive data in combination with a local differential privacy and parameter confusion technology, and meanwhile, cross-domain fusion of threat features and attack surface blind spot detection are realized through a layered distillation verification system. And the APT attack detection rate is improved.

The invention provides a cross-platform education data privacy protection analysis system, and relates to the technical field of data privacy protection. The cross-platform education data privacy protection analysis system comprises a multi-modal data acquisition unit, a dynamic privacy analysis engine, a federal learning processing core module, a block chain enhanced access control system, a privacy watermark traceability module and a compliance autonomy unit. Dynamic coupling of scene sensitivity and data protection intensity is realized through a dynamic privacy risk scoring equation and an adaptive differential privacy noise equation; the problems of privacy disclosure, compliance verification and traceability and responsibility investigation in cross-platform education data sharing are solved by combining federal learning, block chain evidence storage and privacy watermarking technologies. According to the method, the privacy risk assessment precision is remarkably improved, the model precision loss is reduced, full-life-cycle data security management and control are supported, and the method is suitable for multiple scenes such as K12 education and college educational administration.

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 financial privacy security alignment method and system based on federated learning and adversarial training, and the method comprises the steps: enabling a plurality of financial institution clients to obtain local financial data, and carrying out the semantic analysis and sensitivity grading; constructing a federated learning framework, performing homomorphic encryption on a local model gradient by adopting Paillier, uploading the local model gradient to an aggregation server for secure aggregation, and generating global gradient update parameters; dynamically selecting a desensitization strategy based on the business scene weight and the data sensitivity, and executing field-level desensitization processing on the financial data; an adversarial sample is injected in local model training, and a task loss function and an adversarial loss function are jointly optimized; and mapping compliance terms into technical rules, and completing privacy protection and compliance alignment. According to the method, data privacy protection is enhanced, the data leakage risk is effectively reduced, and financial data privacy security is ensured. Safe transmission and effective aggregation of data are ensured, and the global model training efficiency and safety are improved. An adversarial sample is injected, and high accuracy and stability are kept.

The invention provides a zero-set differential privacy self-adaptive federated learning system oriented to Non-IID data. The system comprises a central server and a plurality of clients, the central server maintains a global contribution value list in the federation training process and sends contribution values back to the corresponding clients; according to the contribution values, clients meeting conditions are screened out through a client filtering mechanism and are used for carrying out gradient weighting and generating global model updating; in local model training, each client uses a self-adaptive sliding window mechanism to divide a data set sampling and training range, selects an optimal learning rate through a self-adaptive learning rate selection algorithm, selects a gradient cutting threshold value of each round by using a self-adaptive gradient cutting threshold value mechanism, and performs local model training. And the privacy budget allocated to each round of gradient updating is adaptively adjusted through an adaptive differential privacy federated learning algorithm according to the received contribution value. According to the method, the problem of how to consider both data privacy protection and model precision optimization in a non-independent identically distributed (Non-IID) data environment is effectively solved.

The invention discloses a medical data privacy protection method and device, and relates to the field of medical data security. The method comprises the following steps: acquiring medical data feature information of a plurality of medical institutions to form a medical data feature map; constructing a hierarchical federal learning framework based on the medical data characteristic spectrum; performing parameter aggregation optimization through a convolution weighting method of a recursive context guide network; a dynamic differential privacy budget allocation strategy is combined with a multi-first-choice Lambda weighted list DPO technology to optimize a noise injection process, and adaptive differential privacy protection is realized; and in combination with a homomorphic encryption technology, performing multi-party calculation through a security aggregation protocol to obtain a medical data privacy protection analysis system. According to the method, efficient cooperative analysis is carried out while the privacy of the medical data is protected, the problems that in the prior art, simple parameter exchange may cause model inversion attack, special optimization for medical scenes is lacked, and a supervision and auditing mechanism is imperfect are solved, and the utilization efficiency and safety of the medical data are improved.

The invention discloses a data compliance privacy protection method based on a block chain, and the method comprises the following steps: S1, building a block chain network, and uploading original data; s2, classifying original data, marking sensitive data and encrypting the sensitive data; s3, dynamically distributing authority, and adjusting authority rules in combination with an immune mechanism; s4, completing permission condition verification, and storing a verification result and a log to the block chain network; s5, optimizing an auditing strategy, generating an auditing task and allocating resources; s6, cooperatively completing the auditing task, generating a global verification result and recording the global verification result; s7, adjusting an authority verification and auditing process, and generating a compliance report; and S8, recording a complete permission verification and auditing process. According to the method, the block chain technology is combined with dynamic authority management, privacy multi-party calculation and multi-scale modeling, so that data privacy protection, compliance management and high efficiency, safety and transparency of cross-regional collaboration are realized.

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.

According to the face data privacy protection method and device provided by the embodiment of the invention, a credible authority management mechanism is constructed through a distributed network and a smart contract, and a multi-dimensional feature extraction scheme fusing geometric features and texture features is innovatively designed. The system adopts data fragmentation and homomorphic encryption technologies to carry out security processing on feature vectors, and dispersedly stores encrypted data in a block chain network, so that decentralized management of the feature data is realized. According to the method, feature comparison and identity verification are carried out in a ciphertext domain, the whole process is traceable in combination with a timestamp and an operation record, the problems of data security and privacy protection in a traditional face recognition system are effectively solved, and a safe and credible technical solution is provided for the field of biological feature recognition.

The embodiment of the invention discloses a medical data privacy protection method and a computer device. The method comprises the following steps: firstly, acquiring medical industry laws and regulations and target medical institution data asset information; building a logic relation graph containing data classification and security level according to laws and regulations, and importing the laws and regulations information into a first graph database to obtain a laws and regulations graph; extracting a logical relationship in the data asset information, constructing a data logical relationship graph containing a library table column structure, and importing the data logical relationship graph into a second graph database to obtain an asset graph; business process information is collected, and a data flow relation is identified to perfect an asset map; analyzing the name and content of the data assets to obtain a classification and grading analysis result; on the basis of the result, constructing a heterogeneous graph neural network model by using a graph attention network, establishing a mapping relationship between the two maps, and updating the asset map; and finally, carrying out privacy protection on the data assets according to the updated asset atlas. According to the method, the accuracy and efficiency of medical data classification and grading can be improved, and the privacy protection capability is enhanced.

The invention discloses a federated learning driven trusted data space cross-domain cooperative computing method, and relates to the technical field of data processing, and the method comprises the following steps: carrying out access trust verification on a plurality of data providing domains according to a dynamic trust mechanism; after the verification is passed, accessing each data providing domain into a trusted data space for local training, and generating a local model; performing cross-domain cooperative calculation on the plurality of local models, and establishing an initial global model; and carrying out loss generalization evaluation on the initial global model, and triggering a differential feedback learning module to carry out iterative reinforcement to generate a global reinforcement model. According to the method, the technical problems of limited data privacy protection, trust missing among different mechanisms and insufficient model generalization ability of cross-domain data cooperative calculation in the prior art are solved, multi-party cooperation is realized on the premise of no data leakage through a dynamic trust mechanism and differential privacy protection, and the reliability of the cross-domain data cooperative calculation is improved. And the precision and generalization ability of the global model are improved.

The invention discloses an electric power big data privacy protection method and system based on federated learning, and belongs to the technical field of data processing, and the method comprises the steps: obtaining a historical session record set of an electric power user, carrying out the standardization preprocessing of the historical session record set, and obtaining a standardized session data set, extracting a time sequence feature set from the standardized session data set, calling a pre-configured federal learning framework to carry out privacy protection processing on the time sequence feature set, generating a privacy enhancement feature set, generating a power data privacy protection result based on the privacy enhancement feature set, and sending the power data privacy protection result to the server. And feeding back the power data privacy protection result to the power service system. According to the method, the privacy security in the data use process is guaranteed, the utilization value of the power big data in the unstructured session scene is improved, and the problem of adaptability of a traditional power privacy protection technology in unstructured session data processing is effectively solved.

The invention provides a data privacy protection method for a data governance system, and belongs to the technical field of data governance, and the method comprises the steps: carrying out the cross verification of multi-source feature data and the unique identification information of an object collected on site, generating an original data set, carrying out the sensitive information recognition and grading, and generating the preprocessing data with a sensitive grade label; core identification information in the preprocessed data is disassembled to generate standardized desensitized data conforming to privacy protection, a bidirectional encryption mapping relation is established between object unique identification information collected on site and the standardized desensitized data, an encryption index is formed, the encryption index and preset multi-dimensional compliance data are fused, and a data fusion result is obtained; generating standardized fusion data; and based on the access token, generating a differential authorization data set divided according to permission granularity, performing privacy disclosure risk assessment, generating a risk level, performing privacy processing on the risk level, and outputting the risk level to a risk control system. And the data management efficiency is improved.

The invention relates to the technical field of data privacy protection, in particular to a data privacy protection management system based on artificial intelligence, which is characterized in that a resource library building module is used for extracting privacy protection knowledge from multi-source data and building a structured resource library; and the privacy classification module classifies data privacy information and evaluates a sensitive level through intelligent identification and analysis, and provides a basis for a differential protection strategy. The privacy protection module fuses privacy types and sensitive levels, constructs a core policy engine, and comprises a privacy desensitization sub-engine, an access control sub-engine and a privacy risk prediction sub-engine, so as to realize automatic generation and dynamic adaptation of protection policies. And the privacy management module defines an execution sequence and a collaboration mechanism of each sub-engine according to a pre-configuration process, generates a privacy protection method covering the full life cycle of the data, ensures end-to-end collaboration management of links such as data acquisition, storage, use and sharing, and remarkably improves the compliance, efficiency and risk prevention and control capability of data privacy protection.

Disclosed in the present invention are a large language model federated fine-tuning method and apparatus based on gradient compression. The method comprises the following steps: constructing, on the basis of a gradient tensor generated during fine tuning of a large language model, a raw data set having a time series relationship, performing inference by means of an autoencoder to obtain a reconstructed gradient data set, and constructing a reconstruction loss function to optimize the autoencoder; and initializing a base model of the large language model as a global model at a server end, the server end updating the global model to a client, using a pre-trained encoder to obtain a compressed gradient at the client, and at the server end, using a pre-trained decoder to decode and aggregate the compressed gradient, and then updating the global model. The present invention can improve the fine-tuning efficiency of the large language model and reduce computing resource requirements while ensuring data privacy protection, and is suitable for application scenarios such as communication optimization improvement and privacy protection enhancement in the process of scientific computing-oriented large model fine-tuning and training.

The invention provides a data privacy protection method and device for an industrial internet platform and a medium, and relates to the field of data privacy protection.The method comprises the steps that sensitive field identification is conducted on multi-source shared data in the industrial internet platform, data desensitization processing is conducted on sensitive information, and multi-source desensitization data are obtained; adding controllable noise to the multi-source desensitized data according to preset privacy protection parameters to obtain multi-source privacy data; and data sharing and data processing are carried out according to the multi-source privacy data, and in the sharing process, data encryption is carried out on data to be processed by adopting a homomorphic encryption technology. The method aims at solving the technical problem that in the traditional industrial multi-source data sharing and processing process, the privacy and safety of data cannot be effectively guaranteed, through three technical means of data desensitization, differential privacy and homomorphic encryption, multi-party cooperation and calculation are allowed while privacy is guaranteed, and the availability of the data is improved.

The invention discloses a psychological disease pre-diagnosis information processing method and system, and the method comprises the steps: collecting a multi-modal behavior signal and a physiological parameter signal of a user, and carrying out the differential privacy protection processing based on edge calculation and the noise separation processing based on wavelet transform, obtaining a standardized behavior feature sequence and a time sequence physiological feature vector; extracting behavior node features and psychological state markers from the standardized behavior feature sequence, and constructing a three-dimensional incidence matrix in combination with the time sequence physiological feature vector; and calculating a potential risk probability based on a graph neural network model, generating a pre-diagnosis grading result, fusing objective behavior environment data through a progressive protocol, outputting a personalized intervention strategy, and finally generating a comprehensive pre-diagnosis report. According to the method, accurate modeling of psychological-physiological-behavior dynamic association is realized through dynamic knowledge graph construction and cross-modal fusion analysis, meanwhile, data privacy protection is considered, and the accuracy and practicability of psychological health monitoring are effectively improved.

The invention relates to the technical field of data security, and particularly discloses a data privacy protection and sharing method in a man-machine-object fusion environment, which comprises the following steps: S1, determining a data range and sensitivity, comprehensively sorting and classifying data, pre-judging an affected encrypted data type according to a new technology trend, and planning a strategy in advance; according to the method, data security is ensured by sorting and classifying data, removing and labeling error data, applying strategies such as anti-quantum encryption and the like, then risk assessment and modeling are carried out by virtue of artificial intelligence and big data, identity authentication is carried out by virtue of biological characteristics and behavior analysis, anonymization, desensitization and the like are processed by virtue of an algorithm, and meanwhile, a monitoring range index is determined; the method depends on advanced tool monitoring and rapid response according to abnormity, continuously improves strategies, pays attention to user education, establishes a feedback reward mechanism, formulates a data sharing protocol and actively participates in industrial standard and international cooperation, so as to promote comprehensive development and application of a data privacy protection technology.

The invention provides a federated learning differential privacy method based on Rayleigh divergence and adaptive noise adjustment, and aims to balance data privacy protection and model training performance and improve model accuracy and convergence speed of federated learning on the premise of protecting user data privacy. And the contradiction between privacy protection and model performance in the existing federated learning is solved. The method comprises the following steps: step 1, constructing a privacy loss quantification model based on Rayleigh divergence; 2, deducing a tight upper bound of a Gaussian noise standard deviation; 3, initializing noise parameters of the federated learning system; 4, the client side executes local model training and noise adding; 5, updating the aggregation model of the central server and evaluating the performance; step 6, implementing a self-adaptive noise adjustment decision based on model performance; and step 7, iterating federal learning training until convergence or completion.