3634results about "Communication with homomorphic encryption" patented technology

The invention discloses a transformer area intelligent fusion terminal data processing system based on edge calculation, relates to the field of edge intelligence, and solves the problems of poor real-time performance and poor data exception processing accuracy of an existing intelligent fusion terminal data processing system. According to the method, communication between micro-services is efficiently managed through the container arrangement engine and the service grid, the message transmission delay is reduced, and resource allocation is further optimized through a resource scheduling algorithm based on reinforcement learning, so that system resources are used most reasonably; according to the method, anomaly detection is rapidly and accurately carried out through a space-time heterogeneous fusion network model, hierarchical processing of data and on-cloud training of the model are realized through a cloud-edge collaborative architecture, edge nodes participate in model training through a federated learning mechanism, the model performance is optimized, the transmission time of the data in the network is shortened, and the problem positioning speed is increased; the real-time performance and the reliability of an existing system are greatly improved.

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 data processing, in particular to an artificial intelligence algorithm-oriented model encryption and privacy protection method, which comprises the following steps of: generating a privacy level mapping table through quantum feature extraction and multi-objective optimization, and dynamically screening node subsets with low delay and complementary data distribution by adopting a quantum annealing algorithm, a federated aggregation formula is improved by combining a weight prediction model of Bayesian optimization, and parameter conflicts caused by non-independent identically distributed data are relieved. For the balance problem of privacy protection and model precision, incremental update parameters are generated through quantum hash comparison, violation data streams are blocked in combination with anomaly detection of a quantum support vector machine, and an auditing chain drives a quantum hybrid optimization framework to synchronously adjust encryption strength and noise rules. And the closed loop iteration mechanism feeds back optimization parameters to the data preprocessing and encryption module to form full-link adaptive cooperation from feature dimension reduction, model training to reasoning output, so that global optimization of privacy protection and model effectiveness in a data heterogeneous scene is realized.

A federated distributed computational system enables secure collaboration across multiple institutions for biological data analysis. The system consists of interconnected computational nodes managed by a centralized or decentralized federation manager, depending on the deployment model. Each node contains specialized components that work together to process biological data while preserving privacy. These components include a local computational engine that handles data processing, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships by connecting various data sources, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaborate on complex biological analysis tasks without compromising their sensitive data, enabling breakthrough discoveries through shared computational resources and expertise while maintaining the security, compliance, and confidentiality required in biological research.

A block chain double-chain cold chain traceability management system for meat quality fluctuation belongs to the technical field of traceability management systems and comprises a data acquisition and preprocessing module, a composite index module, a security verification module, a federated learning and AI resource management module, a fragmentation and verification architecture module and an automatic error correction module. According to the method, data chain storage environment data hash and transaction chain record operation logs are innovatively separated through a block chain double-chain architecture, hash fingerprints are synchronized in combination with an anchoring technology, cross-chain consistency is ensured, long-term non-tampering of data is realized through an anti-quantum hash algorithm, resource allocation is optimized through hierarchical storage design, and the method is suitable for large-scale popularization and application. The composite index technology is fused with hypergraph dynamic index to trace production batches, vehicles, warehouses and space-time quadtree dynamic subdivision areas, complex query response time is remarkably compressed, path deviation real-time alarm is achieved, the edge node cache hit rate is increased, and full-link data safety and query efficiency are remarkably improved.

A system and method for adaptive real-time multi-modal compression with dynamic resource allocation provides intelligent compression optimization based on continuously monitored device conditions. The system monitors battery level, CPU utilization, and memory availability while classifying incoming multi-modal data streams comprising image, audio, text, and sensor data to determine processing priorities. Multi-objective optimization balances compression efficiency, reconstruction quality, and energy consumption using evolutionary algorithms that generate optimal parameters for an adaptive variational autoencoder. The autoencoder features dynamically selectable processing complexity, adjustable latent space dimensionality, and modality-specific processing layers. The system automatically switches between operational modes including emergency mode triggered by resource constraints, which applies maximum compression settings and intelligent data triage. Continuous learning adapts compression parameters based on observed performance outcomes, improving future optimization decisions. The system enables homomorphic operations on compressed data and provides enhanced compression performance under varying resource constraints across diverse edge computing applications.

The invention discloses a multi-modal diagnosis and treatment data analysis and auxiliary diagnosis method for internet medical treatment, which comprises the following steps: S1, performing feature extraction and fusion on multi-modal diagnosis and treatment data through a nonlinear mapping algorithm based on quantum state optimization; s2, constructing a self-adaptive dynamic relation network, and capturing a complex interaction relation between cross-domain features in the diagnosis and treatment data; s3, fusing implicit features in the sparse medical data with semantic association; s4, optimizing the diagnosis and treatment decision process by applying a causal inference technology; s5, processing anomalies in the diagnosis and treatment data by using a fusion generative adversarial network and sparse tensor decomposition technology; s6, constructing a decentralized encrypted federated learning network to protect the privacy of the patient; s7, predicting a disease evolution path in real time to form dynamic decision support; and S8, performing accelerated analysis on the high-dimensional feature mode in the diagnosis and treatment data in combination with quantum calculation. The method has the advantages of high data fusion capability, high diagnosis precision and high real-time decision support capability.

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 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 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 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 integrated circuit test and calibration, and discloses a chip test and calibration method and a chip tester. The chip test calibration method is applied to a chip test module and specifically comprises the following steps that S101, a starting signal sent by a user terminal is received, environment initialization operation is executed, and after initialization is completed, a system automatically enters a standby mode and waits for an external trigger signal or a user instruction; and S102, synchronously capturing input and output signal waveforms of the tested chip at a preset sampling frequency through a high-precision current sensor and a voltage sampling circuit. Through a dynamic parameter acquisition and real-time compensation technology, a voltage reference error is effectively reduced, the temperature control precision is superior to + / -0.3 DEG C, signal distortion caused by environmental interference is effectively inhibited, a multi-dimensional calibration model is combined with time domain, frequency domain and statistical characteristic analysis, nonlinear errors and system drift can be dynamically corrected, and the system performance is improved. And the attenuation error of a test signal transmission path is lower than 0.02 dB.

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 discloses an electronic lead seal automatic detection method applied to logistics tracking, and relates to the technical field of Internet of Things safety monitoring. The problems of packaging data loss, tampering risk increase and real-time monitoring failure caused by wireless communication signal interference, transmission delay and state updating lag of an existing electronic lead seal are solved. The method comprises the following steps: fusing multi-physical field sensing data through a space-time correlation sampling algorithm, and constructing an anti-interference characteristic matrix; evaluating communication quality based on the dynamic probability network model, and triggering a multi-path fragmentation concurrent transmission strategy to avoid signal attenuation; a hybrid reasoning model is deployed at an edge end to screen key event data, and the transmission efficiency is optimized in combination with differential coding and an IEEE 1588 clock synchronization mechanism; the cloud end adopts a space-time diagram fusion analysis model to carry out cross-modal abnormal association scoring, corrects misjudgment and updates an edge model; according to the invention, the communication reliability, the real-time transmission efficiency and the anomaly detection precision of the lead sealing state data in a complex environment are obviously improved.

A spatiotemporal modeling system for Personal Health Database (PHDB) platforms integrates diverse health data types into a comprehensive 4D model of an individual's health status. By combining genomic, imaging, clinical, and real-time health data, the system creates a dynamic, time-based representation of the user's anatomy and physiology. This model enables real-time analysis, pattern recognition, and predictive forecasting of health outcomes. The system preprocesses and aligns data from various sources, constructs a detailed spatial framework, and continuously updates the model with new inputs. Through interactive visualizations, it provides users and healthcare providers with intuitive, personalized insights for improved health management and decision-making.

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 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 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 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.

Some embodiments are directed to a computer-implemented method of determining encrypted computation parameters for carrying out an encrypted computation on noisy ciphertexts. A computation graph is divided into multiple subgraphs, defined by a type and by instantiation parameters for the type. Respective sets of encrypted computation parameters are defined for the respective types. An optimization of the encrypted computation parameters is performed to minimize a computational cost of carrying out the encrypted computation according to the encrypted computation parameters. The encrypted computation parameters are constrained to satisfy a noise constraint on ciphertext noise while carrying out the encrypted computation. The noise constraint is based on respective noise constraints for respective subgraphs, defined by a noise constraint function for the type that takes at least the encrypted computation parameters for the type and the instantiation parameters of the subgraph as input.

A knowledge management system may receive a set of data instances. The system may extract a plurality of entities from the set of data instances. The system may convert the plurality of entities into a plurality of entity embeddings, each entity embedding representing an entity in a latent space. The system may generate a reference embedding that has the same length as the plurality of entity embeddings. The system may compare, for each value in each entity embedding, the value to a corresponding value of the reference embedding. The system may generate a plurality of entity fingerprints, each entity fingerprint corresponding to an entity embedding, each entity fingerprint comprising Boolean values that are generated based on comparing values in each entity embedding to corresponding values of the reference embedding. The system may store the plurality of entity fingerprints to represent the plurality of entities.

A method for performing a bootstrapping operation in cryptographic application, includes receiving in an accelerator one or more input ciphertexts used in the cryptographic application to be bootstrapped and iteratively processing one or more accumulator variables in function of portions of the input ciphertexts; multiplying, within each iteration, the processed accumulator variables with a bootstrapping key element belonging to a bootstrapping key comprising a plurality of bootstrapping key elements, said bootstrapping key element taken from a bootstrapping cache memory in the accelerator; while performing the multiplying for each of the one or more accumulator variables in turn, loading into the bootstrapping cache memory from an external memory a next bootstrapping key element of the plurality of bootstrapping key elements to be used in a next iteration of the bootstrapping operation.

The invention provides an equipment corrosion thinning life prediction method and system, and relates to the technical field of life prediction, and the method comprises the steps: deploying edge computing nodes at a plurality of equipment sites to collect corrosion data, carrying out the encryption through employing a differential privacy algorithm, and constructing a federated learning network for model training; and the central server extracts prediction experience through knowledge distillation to form knowledge priori, the edge nodes predict the corrosion rate and the residual life based on real-time data, and the server generates a comprehensive evaluation report and continuously optimizes the model. According to the method, safe sharing and collaborative prediction of the equipment corrosion data are realized, the prediction precision is improved, and the data privacy is protected.