314 results about "Data availability" patented technology

The invention discloses a data text desensitization method and system for economic big data, and particularly relates to the field of text desensitization. In a data receiving stage, heterogeneous data sources are accessed through multiple channels, structured data are subjected to field-level standardization processing, and unstructured texts are subjected to OCR conversion and semantic segmentation; the method comprises the following steps: constructing a data consanguinity graph, identifying sensitive entities based on a pre-trained NER model, realizing three-layer hierarchical management in combination with dynamic weight calculation, and dividing the entities into a core layer, an association layer and a non-sensitive layer; an entity association network is constructed through a knowledge graph technology, a privacy association strength coefficient is quantified, and cross-entity desensitization consistency control is realized; a privacy risk assessment model is adopted to calculate a residual risk value, an adaptive enhancement mechanism is triggered when the residual risk value exceeds a threshold value, data availability is maintained through a utility assessment unit while privacy is protected, and dynamic balance between risk and utility is formed.

The invention relates to the field of data query, and particularly provides a medical big data query method and system based on dynamic desensitization, a terminal and a medium, and the method comprises the steps: collecting multi-source heterogeneous medical data, carrying out semantic normalization, constructing a unique identifier of a patient, and generating standardized intermediate plaintext data; constructing an adaptive isomorphic index structure based on the data, constructing a semantic hash B + tree index for structured data, constructing a feature vector index for unstructured data, and dynamically optimizing index hierarchy and parameters according to access frequency; and after a query request is received, the permission context is analyzed, intermediate plaintext data is positioned and read by using the index, and a differential privacy protection strategy is dynamically applied according to the access permission. According to the method, the problem of balance between privacy protection and data effectiveness as well as between retrieval efficiency and storage cost in medical data processing is effectively solved, and data availability and access performance are improved while safety compliance is guaranteed.

The invention discloses a dynamic privacy protection closed-loop control method and system for mobile edge crowd sensing. According to the method, information related to a communication channel and data content is obtained through risk perception, and a multi-dimensional comprehensive privacy risk score is calculated; then, dynamically determining a value of a differential privacy budget epsilon according to the risk score by utilizing a reinforcement learning algorithm in a strategy decision-making stage; then, performing noise addition disturbance on crowd sensing data according to the epsilon in a privacy protection execution stage so as to realize a preset privacy protection level; then, the privacy leakage risk and the data validity of the disturbed data are evaluated in the effect verification stage, and an evaluation result is obtained; and finally, privacy control strategy parameters are adjusted according to an evaluation result in a feedback optimization stage, and feedback is applied to a risk perception and decision process of a next cycle, so that closed-loop control is formed, and a privacy protection effect is continuously optimized. According to the method, the user data privacy security can be improved, and the data availability and the energy consumption efficiency are both considered.

The invention discloses a data privacy protection method for an industrial internet platform, and relates to the technical field of data security and privacy protection. According to the method, the sensitive information is accurately identified and deeply analyzed through the sensitive information feature library and the hierarchical matching algorithm, the problem of insufficient accuracy and flexibility during large-scale data processing is solved, the accuracy and reliability of data desensitization are remarkably improved, personal privacy is effectively protected, data availability is maximized, and the method is suitable for large-scale data processing. A hierarchical processing algorithm and a self-adaptive desensitization rule base are utilized, desensitization rules are dynamically adjusted according to dynamic access requirements and sensitivity levels of data, data security and availability are balanced, accurate protection under different scenes is ensured, system adaptability and flexibility are improved, and the data access process is monitored in real time, anomaly detection and rule verification are performed, so that the data access efficiency is improved. And the desensitization rule is dynamically adjusted, so that the security and reliability of the system are enhanced, the user credibility is improved, and the transparency and credibility of the data processing process are ensured.

The invention relates to the technical field of information security, in particular to a user information protection method and device based on a dynamic desensitization strategy, and the method comprises the steps: determining sensitive data and a sensitivity score thereof through a named entity recognition model and a sensitivity scoring algorithm, extracting scene parameters, and determining a scene risk score through a risk quantification algorithm; and then a dynamic desensitization strategy generation algorithm is called to generate a target desensitization strategy, and finally, the strategy is used for desensitization and a reversible desensitization data identifier is generated. Through the series of steps, the desensitization strategy is dynamically generated according to different use scenes, the problem of scene adaptability deficiency caused by static desensitization strategy stiffness is solved, meanwhile, the reversible desensitization reduces the security risk caused by reverse cracking of rules, excessive desensitization is avoided, and the data availability is improved.

The invention relates to the technical field of industrial internet-of-things data security, in particular to an edge calculation differential privacy industrial internet-of-things data desensitization verification system and method.According to the system and the method, edge nodes are divided in a three-dimensional mode according to resource capacity, function positioning and privacy requirements, differential differential privacy parameters are formulated in combination with data attributes and leakage influences, and the safety of the industrial internet-of-things data is improved. And a dynamic adaptive grid is matched to realize hierarchical alignment, scene binding and elastic reconstruction, so that a cross-hierarchical privacy risk is avoided, and verification logic is simplified. Noise intensity is dynamically corrected by integrating multi-dimensional factors based on grids, lightweight, medium and deep hierarchical desensitization is designed for three types of nodes, and privacy protection and data availability are balanced. A privacy security and data availability two-dimension, terminal-gateway-area three-level verification chain is constructed, the desensitization effect is comprehensively evaluated, closed-loop iteration is achieved through hierarchical judgment and accurate adjustment, and industrial scene requirements are efficiently met.

The invention discloses a differential privacy data set distillation method and system based on image generation data, and belongs to the technical field of data privacy protection and machine learning. The method comprises the following steps: firstly, synthesizing a synthetic data set meeting Gaussian differential privacy, training a feature extractor on the synthetic data set, and finely adjusting an expert model by using original data differential privacy; multiple rounds of iterative optimization are carried out on the distillation data set initialized according to the classes, wherein a feature extractor is randomly selected according to the classes in each round to align the features of the original data added with the noise and the distillation data, and an expert model is utilized to align the semantics of the distillation data hard labels and the semantics of the synthetic data soft labels; and finally outputting a distillation data set meeting the total privacy budget. According to the method, effective priori is provided by utilizing generated data, extra noise injection in the alignment process is reduced, convergence is accelerated, and better privacy protection and data availability balance compared with a previous method is realized under the same differential privacy budget.

The invention relates to the technical field of bridge detection, and discloses a detection data acquisition system and method for bridge support design, and the method comprises the steps: constructing a slippage trend prediction model with a disturbance weight gating structure, fusing historical slippage trajectory data and pressure gradient data, and carrying out the prediction of the slippage trend; the time sequence modeling capability of nonlinear response of the sliding support in a vibration state is effectively enhanced, the perception precision of the model on short-time disturbance and inertia reversal behaviors is improved, then a disturbance change rate sequence is constructed based on a predicted sliding trend sequence, a disturbance scoring factor is calculated by using trend fluctuation amplitude and direction reversal times, and the disturbance change rate sequence is calculated. According to the method, the disturbance time window is dynamically marked, so that the problem of lack of support disturbance behavior dynamic division means in the prior art is solved, finally, different filtering processing modes are adopted for the disturbance time window and the non-disturbance time window, and differentiation processing is performed on real-time strain data. And the availability of the strain data under the low-frequency disturbance background is improved.

The invention discloses a customer information storage management method and system based on a block chain, and relates to the technical field of distributed data evidence storage, and the method comprises the steps: collecting terminal customer information, generating an encrypted data packet, carrying out erasure coding, and generating an encrypted data fragment set; carrying out distributed storage based on the encrypted data fragment set, and calculating a data availability commitment at the same time; executing availability sampling according to the data availability commitment, and generating a fragment validity proof; based on the fragment validity proof, performing format and validity verification to obtain a to-be-processed proof set; and generating a recursive aggregation proof according to the to-be-processed proof set, and submitting the recursive aggregation proof to the block chain to verify and update a proof storage state. And cryptographic authentication is carried out on the persistent evidence storage state of the data assets, so that the dual beneficial effects of ensuring data privacy and credible evidence storage are achieved.

The invention discloses a personalized customization data mining and analysis system based on artificial intelligence, and the system comprises the following modules: an edge data collection module which is used for generating a structured multi-source heterogeneous data set; the high-order data tensor construction module is used for constructing a high-order data tensor based on the structured multi-source heterogeneous data set; the heterogeneous tensor decomposition module is used for extracting a potential feature matrix and a core tensor; the feature fusion and unified expression module is used for organizing all the fused feature vectors according to a time sequence to form a unified feature expression sequence; the dynamic clustering analysis module is used for generating a clustering evolution diagram; the clustering stability evaluation and feedback module is used for obtaining a fusion feature vector in a cluster structure mutation state; and the abnormal data labeling module outputs an abnormal data labeling result set. According to the method, the overall data availability and maintainability of the system are greatly improved.

The invention discloses a multi-field data desensitization method and device, equipment and a medium, and relates to the technical field of data security, and the method comprises the steps: carrying out the semantic feature extraction of a field name and a field value of a target field through a target model, obtaining a field name semantic feature and a field value semantic feature, fusing the field name semantic feature and the field value semantic feature to obtain a target semantic vector; determining target association strength between the target semantic vectors corresponding to any two target fields, and constructing a cross-field semantic association matrix based on the target association strength; determining a target field combination meeting a preset high semantic correlation standard from a database based on the cross-field semantic association matrix, performing sensitivity scoring on the target field combination to obtain a sensitivity score, and determining a data type corresponding to the target field combination by using a preset classification model; and desensitizing the target field combination according to the sensitivity score and the data type. According to the method, the data availability is guaranteed while the privacy protection level is improved.

The invention discloses a user track privacy protection method and system fusing a generative model and three-stage personalized differential privacy, and relates to the technical field of data security, and the method comprises the steps: obtaining real user track data, and carrying out the preprocessing of the real user track data to form standardized track representation; training a depth generation model to generate synthetic trajectory data; performing density clustering on the synthetic trajectory to obtain a feature region and calculating a region centroid; point-level privacy budgets are distributed according to three stages of the area proportion, the centroid distance and the global density; constructing an adaptive elliptical disturbance domain based on the regional covariance, sampling candidate confusion points in the disturbance domain and applying road feasibility screening; and calculating a score based on the privacy budget and the utility function, and sampling replacement points by adopting an index mechanism to output a privacy protection trajectory. According to the technical scheme, the track structure continuity and the road semantic consistency are remarkably improved under the condition that the differential privacy constraint is met, and the privacy intensity and the data availability are both considered.

The invention discloses a child activity data desensitization processing method and system based on differential privacy, and relates to the technical field of privacy data protection, and the method comprises the steps: carrying out the time synchronization and unit normalization of multi-source data from a wearing device and an environment sensor, and building a unified multi-dimensional time sequence matrix; then, introducing a change point detection method to dynamically segment the time series data, so that the data is divided into event segments with clear behavior semantics; a sensitivity scoring mechanism is designed, factors such as activity types, time, places and behavior frequencies are comprehensively considered, privacy risk quantification is performed on each event segment, adaptive privacy budget allocation is implemented based on a scoring result, and key protection of sensitive data segments and efficient utilization of conventional data segments are realized; after differential privacy noise is injected, time sequence consistency correction and cross-feature correlation correction are combined. Therefore, differentiated and refined protection of the child activity data is realized, and privacy protection and data availability are effectively balanced.

The invention discloses a data protection method and system based on federated learning and homomorphic encryption, belongs to the technical field of data security, and aims to solve the technical problem of how to effectively protect user privacy while ensuring data availability. Comprising the following steps: a medical data node performs model training on a local AI model deployed locally based on a local data set, and uploads an encryption model gradient to a federal coordination server; the global parameters issued by the federal coordination server are decrypted, and gradient updating is performed on the local AI model based on the decryption result of the global parameters; calling the trained local AI model to perform model reasoning on the basis of the reasoning request for the third-party user who passes the identity authentication and has the access permission, and returning a reasoning result to the third-party user; and based on the auditing log, analyzing the running state and resource consumption of the medical data node according to a predefined auditing rule, and generating alarm information based on the abnormal operation.

A method for managing data availability includes making a first determination by a first security module (FSM) that a first storage area network (SAN) infrastructure in a first data center has experienced a failure. The method also includes generating a secure string based on a first configuration parameter. Further, the method includes appending the secure string to a SAN failure notification to generate a secure string-appended request. In addition, the method includes sending the secure string-appended request to a second data center, wherein the second data center is selected based on a second configuration parameter. Moreover, the method includes making a second determination that the encrypted secure string-appended request is valid. Further, the method includes offloading processing of requests sent to the first data center using the second data center.

The invention provides a privacy protection and access control method and system based on unstructured metadata, and relates to the technical field of data processing, and the method comprises the steps: obtaining to-be-processed time sequence metadata of a plurality of data source nodes, calculating sensitivity characteristics and scores, and dividing sensitivity levels; a differential privacy budget is allocated according to the sensitivity level for processing; setting hierarchical access permissions and desensitizing visitor information; and analyzing access behavior characteristics and setting a differential privacy threshold to perform abnormal access judgment. According to the method, refined grading protection on the time sequence sensitive data is realized, the privacy protection efficiency is improved, and meanwhile, the data availability is ensured.

The invention discloses a route determination method, system and device and a storage medium. The method comprises the steps of obtaining route notification data and a candidate route set; constructing a knowledge graph based on the route notification data, and calculating a reputation value of each autonomous domain node based on the knowledge graph; and based on the reputation value of each autonomous domain node, screening out a credible route set from the candidate route set, performing reputation weighted scoring on paths in the credible route set, and selecting the path with the highest score as the optimal route. Semantic organization is carried out on the autonomous domain, the routing prefix and the strategy relation through the knowledge graph, the data availability and analysis efficiency are improved, dynamic evaluation of the autonomous domain reputation is achieved in combination with neighbor recommendation, and the accuracy of credibility judgment is improved; a reputation evaluation result is directly applied to path selection, untrusted nodes and paths are effectively filtered, potential attack risks are reduced, and the overall security and stability of a routing system are improved.

The invention discloses a modal fusion intelligent video abnormal behavior monitoring method, and relates to the technical field of intelligent security monitoring, and the method comprises the following steps: collecting video modal data through a video camera, collecting sound modal data through an audio sensor, and obtaining environment state modal data through an environment state sensor; denoising processing is carried out on video data, noise reduction and filtering operation is carried out on sound data, and normalization processing is carried out on environment state data, so that the data availability is improved. By constructing an abnormal behavior analysis model and adopting a unique layered structure, compared with a traditional simple model, the complex incidence relation between multi-modal data can be effectively processed, and in a scene where personnel gather and are accompanied with noisy sound, the model can integrate personnel actions and postures in a video and sound features in an audio frequency, so that the model can be used for analyzing the abnormal behavior. Whether abnormal behaviors such as noisy and fighting occur or not is accurately judged, the accuracy of abnormal behavior judgment is greatly improved, and the misjudgment rate and the missed judgment rate are reduced.

The invention relates to the field of fault detection, and discloses a fault diagnosis system for a pipe network, and the system comprises a data collection module which is used for obtaining pipeline operation data from an SCADA system, a simulation environment, an intermediate database, AP I transmission, and manual inspection data entry; the data preprocessing module is used for performing cleaning, normalization and structured storage on the acquired data, including data availability inspection, basic statistic calculation and box plot drawing; the data analysis module is used for constructing a scoring model according to the pipeline operation data and determining a data distribution type according to the scoring model in combination with a dynamic threshold judgment mechanism; the model development module is used for selecting an optimal model according to the data distribution characteristics, supporting integration of various models, generating and evaluating a plurality of model results, and outputting a visual model result by adopting a hybrid modeling strategy; and the real-time early warning module is used for monitoring the operation state of the pipe network in real time, sending out early warning information based on the diagnosis result and carrying out visual display.

The invention discloses a lightweight differential privacy federal learning method for cross-domain management and control of power data. The method comprises the following steps: S1, performing initialization setting; s2, performing local training and parameter processing on the client; s3, uploading and aggregating parameters; s4, privacy loss evaluation and parameter adjustment; s5, pruning and compressing the model; s6, privacy protection and full mask multiplexing based on secret sharing; and S7, preventing collusion attacks. According to the method, the calculation complexity is remarkably reduced, and the core contradiction that privacy protection and model practicability are difficult to consider in cross-domain cooperation of power data is solved; the problems of high communication overhead and insufficient computing power of edge equipment during massive power data processing of traditional federated learning are solved, and the real-time performance and expandability of cross-domain cooperation are remarkably improved; while data availability is guaranteed, complex threats such as multi-client collusion and man-in-the-middle attack are effectively handled, and a privacy protection framework with a higher security level is provided for multi-agent cooperation of a power system.

The invention discloses a blockchain zero-knowledge proof collaborative credit risk control data privacy protection query method, and belongs to the technical field of financial science and technology and information security. According to the method, a four-layer architecture of a feature importance analysis layer, a zero-knowledge proof verification layer, a block chain intelligent contract layer and an audit log layer is constructed, so that the contradiction between privacy protection and data availability in a traditional credit risk control system is solved. According to the system, an SHAP value is adopted to calculate feature importance, a zk-SNARKs verification circuit is deployed for high-sensitivity features, zk-STARKs lightweight verification is adopted for low-sensitivity features, and differential privacy protection is achieved. And dynamically adjusting the complexity of a verification algorithm according to the credit risk score of the user, and realizing dynamic authority control based on a verification result through an intelligent contract. The verification metadata is recorded to the block chain to form a tamper-resistant auditing log, and it is ensured that the query process is transparent and traceable. According to the method, the real-time risk control requirement is met while the privacy of the user is protected, and a compliant and efficient data query solution is provided for financial institutions.

The present application relates to the technical field of computational imaging, and in particular to a scanning light field self-supervised network denoising method and apparatus, an electronic device, and a storage medium. The method comprises: obtaining scanning light field data; preprocessing the scanning light field data to obtain preprocessed data; and constructing a self-supervised denoising network on the basis of the preprocessed data until a preset iteration stop condition is reached, and thereby obtaining a final self-supervised denoising network that is used to perform scanning light field self-supervised network denoising. In this way, the invention solves problems in the prior art such as limited data availability due to reliance on paired high-quality data having the same or similar content, which in turn increases data acquisition difficulty and leads to increased cost; and the inability to perform denoising with single-frame light field images due to reliance on temporal information and the need for information from multiple frames, which in turn leads to poor responsiveness and reduced adaptability in practical applications.

The invention relates to the technical field of intelligent operation and maintenance of photovoltaic power station equipment, and discloses a photovoltaic power station equipment operation data analysis method and system based on the Internet of Things. The operation data analysis method is applied to data analysis equipment and specifically comprises the following steps that S101, a data analysis request is received, and the data analysis request collects parameter data of current, voltage, temperature, irradiance and environment humidity in real time through Internet of Things sensor nodes deployed on a photovoltaic module, a combiner box, an inverter and a meteorological station; according to the method, the problem of space-time misalignment of multi-source heterogeneous data is solved, the outlier recognition accuracy is improved, the data availability rate is greatly optimized, the limitation of traditional single-dimensional analysis is broken through by innovative four-dimensional feature engineering, the ripple spectrum entropy in the electrical features and the response delay time in the environment coupling features are effectively improved, and the reliability of the multi-source heterogeneous data is improved. And by combining a thermal-electric propagation model constructed by GCN, the detection rate of early faults such as microscopic subfissure is increased from 60% to 95%.

The invention provides a laser radar saturation waveform reconstruction method based on a hybrid neural network, and the method comprises the steps: carrying out the normalization processing of laser radar full waveform data, carrying out the traversal through employing a sliding window with a fixed length, and extracting a sub-waveform segment with a large sum of amplitudes in the sliding window; respectively extracting local mutation features and global morphological features of each sub-waveform segment by using a double-branch convolutional network, fusing the extracted features by using a multi-head attention fusion network, and detecting abnormal sub-waveform segments and corresponding saturated sampling points; jointly establishing a reconstruction network based on an encoder structure and a residual convolution structure, and reconstructing the amplitude value of the saturated sampling point to obtain a reconstructed sub-waveform segment; and inserting the reconstructed sub-waveform segments into corresponding positions of the normalized laser radar full-waveform data, and carrying out reverse normalization operation to obtain reconstructed full-waveform data. The method is high in recognition accuracy and small in reconstruction error, and can remarkably improve the ranging precision of the laser radar and the usability of waveform data.

The invention relates to the technical field of data privacy protection, and discloses a differential privacy protection performance correlation analysis and visualization system, which is used for comprehensively representing multi-dimensional correlation of performance data by collecting employee performance data and carrying out structured preprocessing to construct a four-order tensor. And a differential privacy mechanism is utilized to perform decomposition and noise injection on the tensor, so that the security of sensitive information is guaranteed. In addition, the system evaluates the privacy sensitivity of each node by constructing a factor map, generates an adaptive visualization result in combination with the remaining privacy budget, and dynamically adjusts a display strategy. By recording user interaction behaviors and budget changes in real time, a game strategy optimizes a display mode, and balance between privacy protection and data availability is achieved. The requirements of privacy protection and data display are effectively balanced, a safe, intelligent and dynamically-adjusted performance analysis and visual display tool can be provided for an enterprise, and the capacity of the enterprise in the aspects of data safety and decision support is improved.

The invention relates to the technical field of oil exploration and development, and discloses a drilling speed real-time prediction method only using historical data and controllable data. In order to solve the problem that traditional prediction depends on uncontrollable feedback data and unknown data under the undrilled depth, well drilling parameters are classified in detail, data constraints in real-time well drilling are analyzed, a prediction framework is provided, and missing real-time input is compensated through information in historical data. By integrating previously drilled areas, the model can improve prediction accuracy even when excluding uncontrollable parameters of the undrilled depth. The method emphasizes the importance of combination of a drilling speed prediction method and operation constraints in the real world. The discovery contributes to improving the applicability in drilling operation, provides a feasible solution for predicting the mechanical drilling speed under the actual conditions of limited data availability and complex geological environment, and contributes to intelligent development and management of an oil field.

The invention relates to the technical field of power grid data privacy protection, in particular to a smart power grid data privacy protection system and method based on a homomorphic encryption technology. The system protects data privacy of an intelligent power grid based on a homomorphic encryption technology, and comprises eight modules: a data acquisition module is connected with an intelligent electric meter in a multi-protocol manner in a 15-minute period to adopt electric data; the data filtering and preprocessing module optimizes data quality; the homomorphic encryption module generates a ciphertext capable of being added and multiplied by using an FHE algorithm; the multi-level key management module manages multiple types of keys in the whole life cycle; the secure transmission module transmits the ciphertext by means of a hybrid protocol; the data analysis module decrypts and analyzes the ciphertext in the data center; the decryption module decrypts after authorization; and the auditing and monitoring module records operation and monitors abnormity. According to the invention, it is ensured that the power use data is kept in an encrypted state in the whole life cycle of collection, transmission and analysis, the data is available and invisible, and the data privacy protection level and quantum attack resistance of the smart grid are improved.

The invention discloses a privacy protection type data desensitization method for a big data analysis platform, and relates to the technical field of data security and privacy protection. According to the method, the sensitive information is accurately identified and deeply analyzed through the sensitive information feature library and the hierarchical matching algorithm, the problem of insufficient accuracy and flexibility during large-scale data processing is solved, the accuracy and reliability of data desensitization are remarkably improved, personal privacy is effectively protected, data availability is maximized, and the method is suitable for large-scale data processing. A hierarchical processing algorithm and a self-adaptive desensitization rule base are utilized, desensitization rules are dynamically adjusted according to dynamic access requirements and sensitivity levels of data, data security and availability are balanced, accurate protection under different scenes is ensured, system adaptability and flexibility are improved, and the data access process is monitored in real time, anomaly detection and rule verification are performed, so that the data access efficiency is improved. And the desensitization rule is dynamically adjusted, so that the security and reliability of the system are enhanced, the user credibility is improved, and the transparency and credibility of the data processing process are ensured.

The invention relates to the technical field of data processing, and discloses a multi-modal data encryption fusion method and system, the multi-modal data encryption fusion method comprises the following specific steps: receiving multi-modal data from different sources, the multi-modal data at least comprising image / video data, audio data and text data, according to the multi-modal data encryption fusion method and system, customized encryption strategies are adopted for different data types, such as chaotic mapping visual password protection images / videos, improved AES-CTR optimization audio encryption and homomorphic encryption retention text semantics, the encryption efficiency is improved, and meanwhile, the security of each modal data is ensured; according to the multi-modal data encryption fusion method and system, the features are directly extracted from the encrypted data through the deep learning model, semantic association fusion is realized in combination with an attention mechanism, privacy leakage caused by decryption is avoided, and meanwhile, data availability is kept.

The embodiment of the invention discloses a data desensitization method and device, electronic equipment and a storage medium, and the method comprises the steps: transmitting a first desensitization model to at least one data producer corresponding to a data acquisition request when the data acquisition request sent by a data consumer is received; receiving first desensitization data fed back by at least one data producer, and performing secondary desensitization processing on the first desensitization data to obtain target desensitization data; wherein the first desensitization data is data processed through a first desensitization model; the target desensitization data is fed back to a data consumer, the highly-imitated data subjected to secondary desensitization processing can simulate a real business scene and finely describe business characteristics, the privacy of production data can be protected, the contradiction between data availability and privacy protection is balanced in a cross-mechanism and cross-industry data sharing scene, and the privacy protection efficiency is improved. And the security and availability of cross-mechanism and cross-industry data interaction are improved.