651 results about "Data sharing" patented technology

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 an intelligent building construction operation management method based on big data, and belongs to the technical field of building construction management. Comprising the following steps: step 1, real-time data acquisition of construction site personnel, equipment and environment is realized, and a dynamic information database is established; 2, preprocessing and feature extraction are carried out on the collected multi-source heterogeneous data, and fusion and intelligent analysis of real-time data are achieved; step 3, accurately identifying risk factors and implementing refined early warning and intelligent intervention aiming at a single risk; 4, construction resource configuration is dynamically optimized, the resource utilization efficiency is improved, and meanwhile the cost and the safety risk are reduced; step 5, constructing a block chain data sharing mechanism, and realizing data tracing and intelligent management of a construction full life cycle; and step 6, forming a self-adaptive closed-loop feedback mechanism, and promoting intelligent transformation of construction management from passive response to active prediction.

The invention discloses a steel-concrete hybrid girder bridge operation monitoring and static and dynamic analysis method based on multi-source data fusion, which comprises the steps of 1, structural response data acquisition, 2, multi-source data fusion processing, 3, building a plurality of operation state evaluation models based on parameters acquired by a sensor, and 4, carrying out static and dynamic analysis on the operation state evaluation models. 4, performing static analysis, dynamic analysis and static and dynamic collaborative analysis on the girder bridge based on the comprehensive feature set after multi-source data fusion, and 5, providing data support for the static and dynamic collaborative analysis by the operation state evaluation model through the comprehensive feature set, and meanwhile, evaluating the operation state of the girder bridge. Reversely supplementing the operation state evaluation model with the structural mechanical response characteristics excavated in the static analysis and dynamic analysis processes to form a data closed loop; the method has the advantages that through data sharing and feature interaction, evaluation result cross validation and multi-scale analysis linkage, cooperation of an operation state evaluation model and static and dynamic collaborative analysis is achieved, and a data closed-loop and multi-dimensional evaluation system is formed.

The invention discloses a privacy protection data sharing and cooperative training method and system based on federated learning. The method comprises the steps of receiving software development log data, adaptively judging the sensitivity degree according to a data type, dynamically adjusting noise disturbance intensity according to the sensitivity degree to perform data desensitization, and generating a sensitivity index; selecting a feature extraction strategy, extracting time sequence correlation features from the desensitization data, constructing a dynamic graph structure with a weight, and obtaining a time sequence feature vector through iterative fusion; calculating the time sequence correlation of the time sequence feature vector to obtain a data quality score, and setting a contribution weight based on the quality score to perform parameter aggregation; combining sensitivity indexes with data quality scores to construct a security sharing domain, decoupling global training parameters into knowledge fragments in the domain, formulating a recombination rule, and selectively acquiring the required knowledge fragments by all parties for local training. According to the method, deep collaboration is realized on the premise of protecting data privacy, and the collaboration training effect is improved.

The invention discloses a verifiable medical data query method based on a DAG block chain architecture, and belongs to the technical field of block chains and medical information management. According to the method, medical events such as diagnosis and treatment records and image reports are modeled into DAG nodes, an on-chain topology with time sequence and causal association is constructed by encrypting a hash pointer, dynamic indexing and multi-keyword verifiable query are achieved in combination with an off-chain authentication data structure, a verification object containing a matching node set and a verification path is received, and the verification object is subjected to dynamic indexing and multi-keyword verifiable query. The authenticity, integrity and timeliness verification of the result is independently completed based on the authentication root stored on the chain without depending on a third party. According to the method, through an on-chain and off-chain collaborative storage and incremental authentication updating mechanism, on the premise of ensuring privacy and tamper-proofing of medical data, query efficiency, verification performance and expandability under high concurrency are remarkably improved, communication and resource expenses are lower, and the method is suitable for large-scale popularization and application. The method is suitable for application scenes such as cross-institution medical data sharing, remote diagnosis and treatment and compliance auditing.

The invention provides an enterprise data sharing method and system based on ai driving, and relates to the technical field of ai data processing.The enterprise data sharing method comprises the steps that firstly, field semantic vectors are built based on information such as field names, description, example values and affiliated process positions, and semantic tag normalization is completed through a lightweight model; then, a field generation path diagram is automatically constructed through a behavior log, field static semantics and dynamic process behaviors are fused, and modeling is carried out on field generation dependency; fusing field semantic information and an access request context, including dimensions such as an access role, a module, access time and the like, dynamically generating a sharing control rule through a policy model, and defining a sharing permission, a desensitization mode and a return granularity; mapping the strategy into an actual sharing execution action, and realizing control-level response to a data field; according to the scheme, the problems of semantic inconsistency, process isomerism and strategy rigidity can be effectively solved, and the data sharing precision, safety and intelligent level between enterprises are remarkably improved.

A federated learning system (100) for data protection in data sharing and collaboration, consisting of: a module for data acquisition and local preprocessing that is configured to clean, normalize and standardize local data sets at each participating node without transferring raw data externally; a local model training module configured to train a machine learning model on the pre-processed local dataset; a secure model update and encryption module configured to encrypt and secure model parameters or updates before transmission using privacy protection techniques; a federated aggregation and coordination module configured to aggregate encrypted updates from multiple participating nodes into a global model; a module for monitoring and ensuring data protection compliance, configured to enforce data protection budgets and audit protocols and to ensure compliance with data protection regulations; a performance optimization and resource management module configured to optimize communication, computation, and resource utilization across all nodes; and a module for global model delivery and feedback, configured to redistribute the aggregated global model to participants and integrate performance feedback for iterative improvements.

The embodiment of the invention provides a dynamic knowledge graph-driven cross-department government affair data collaboration method and a dynamic knowledge graph-driven cross-department government affair data collaboration system. The method is applied to the technical field of computer science and technology, and comprises the steps of obtaining cross-department government affair service data and corresponding policy rule data, constructing a data knowledge graph and a policy knowledge graph, performing incremental updating and consistency verification on the knowledge graphs based on preset event information, and extracting a newest service state and a newest policy rule. A minimum necessary data view and an authorization range are generated through joint reasoning, efficient and safe collaboration of cross-department government affair data is achieved on the basis, and accurate authorization and compliance management of data access are achieved. According to the scheme, only necessary data is provided and the access permission is strictly controlled in cross-department government affair data collaboration, so that the data sharing efficiency, security and compliance are remarkably improved, and meanwhile, the risks of unauthorized access and redundant data transmission are reduced.

The invention provides an information extraction processing method and system applied to a data sharing service, and the method comprises the steps: obtaining a historical interaction text set from a cross-department business data sharing system, carrying out the entity relation network construction of the historical interaction text set, generating a multi-dimensional entity relation network comprising entity nodes, relation edges and attribute tags, and carrying out the data sharing service through the multi-dimensional entity relation network. Performing semantic topological graph construction based on the multi-dimensional entity relationship network to obtain a semantic topological graph containing entity association strength and a semantic evolution path; calling a preset zero-knowledge proof protocol to perform access control vector generation on the semantic topological graph to obtain an access control vector containing a topological weight threshold and a node access permission; and performing joint processing on the access control vector and the semantic topological graph through a space-time correlation analysis model to generate a business event template containing a standardized field and a correlation index. According to the invention, the accuracy, security and scene adaptability of information extraction in the data sharing service can be improved.

The invention discloses a gynaecology and obstetrics emergency medical data security sharing system based on a block chain, and relates to the technical field of medical information processing. The method is used for solving the security and timeliness problems of multi-mechanism data sharing in an emergency scene. The method comprises the following steps: firstly, performing grading processing and desensitization on personal identifiers and medical data of patients through a data grading and desensitization module, and outputting data which can be safely shared; then, an on-chain evidence storage module performs windowing processing on the continuous monitoring data flow, extracts key physiological features, generates feature value Hash, constructs data feature descriptors and submits the data feature descriptors to a block chain network; the emergency access token management module realizes dynamic authorization through a smart contract, and generates a temporary access token associated with the data feature descriptor; and finally, the secure decryption and data sharing module verifies the authority based on the hierarchical decryption key, decrypts the data and compares the eigenvalue hash, and shares and records an operation log with an authorization party after ensuring the data integrity, thereby realizing efficient, secure and traceable data sharing.

The invention relates to the technical field of tax data security, and discloses a data sharing privacy protection platform responding to tax digital requirements. The platform comprises a tax data privacy perception layer, a sensitive information positioning mapping layer, a cross-source data security fusion layer, a compliance sharing decision-making layer and an execution state feedback layer. The tax data privacy perception layer obtains multi-source tax information based on a dynamic differential privacy mechanism, generates an initial privacy risk heat map and segments a sensitive area; the sensitive information positioning mapping layer generates a data internal privacy distribution stereogram through a security perception array and time sequence analysis; the cross-source data security fusion layer establishes a space-time mapping relation and generates a privacy disclosure risk score; the compliance sharing decision-making layer converts the score into an executable sharing instruction; and the execution state feedback layer monitors data change, generates an evaluation index and dynamically adjusts a privacy protection strategy. According to the platform, the privacy protection of the whole tax data sharing process is realized, and the accuracy and dynamism of risk identification and strategy adaptation are improved.

The invention relates to a data sharing and exchanging method and system based on privacy computing and a big data technology, and belongs to the technical field of data privacy protection and big data analysis. The method comprises the steps of platform initialization and user registration; preprocessing and encrypting data; authority control and task configuration; initiating and scheduling a calculation task; performing privacy calculation; performing result aggregation and decryption; accessing and feeding back a result; auditing and monitoring; the invention further comprises a module for executing the method. According to the platform, through decentralized architecture design and various privacy computing technologies, data availability and invisibility are achieved, the functions of big data cleaning, analysis and modeling are integrated, data privacy safety is protected, and the data value utilization efficiency is improved.

The invention provides a credible data space construction method and system based on data sharing, and relates to the technical field of data processing.The method comprises the steps that S1, in response to a request of a user for sharing target data, credible preprocessing is conducted on the target data to obtain credible data; s2, establishing a decision-making capability model for a user to autonomously perform credible authorization on the credible data; s3, analyzing a multi-dimensional sharing risk of the trusted data; s4, planning an optimal authorization auxiliary unit sequence based on the multi-dimensional sharing risk and the decision ability model; s5, in the process that the user autonomously performs credible authorization on the credible data, sequentially executing the authorization auxiliary units in the optimal authorization auxiliary unit sequence; and S6, aggregating the credible data subjected to credible authorization to construct a credible data space. According to the method, the credibility and the security in the data sharing process are remarkably improved, and the quality of the shared data and the protection of user privacy are ensured.

The invention relates to the technical field of wires and cables, and particularly discloses a wire and cable test system based on sensor data acquisition, which comprises a data acquisition module, an intelligent analysis module and a function output module. The intelligent analysis module integrates technologies such as multi-modal signal blind source separation, mechanism-data hybrid drive deep learning, federated learning and digital twinborn linkage and self-supervised learning optimization, and forms a layer-by-layer progressive analysis system. Interference components are effectively eliminated through a signal separation technology, meanwhile, the adaptability of the model to different working conditions and different cable types is improved by means of fusion of mechanisms and data, model collaborative optimization under multi-site data privacy protection is realized through federated learning, and a self-supervised learning-driven digital twinborn evolution mechanism is combined, so that the multi-site data privacy protection model is optimized. The problems that a traditional analysis technology is insufficient in generalization ability, simulation precision is prone to drifting, and privacy risks exist in data sharing are solved, and the accuracy of fault recognition and the long-term adaptability of the system are remarkably improved.

The invention belongs to the crossing field of artificial intelligence and material informatics, discloses a multi-layer material knowledge graph construction method and system based on a large model, and solves the problems of low utilization rate of multi-modal information, flat knowledge representation, uncontrollable quality and insufficient entity alignment precision in the prior art. The method comprises the following steps: analyzing material science and technology literatures, extracting multi-modal information of texts, tables and images, and preprocessing the multi-modal information into structured data; constructing a three-layer semantic framework of a concept term layer, a material entity layer and a material data layer; extracting knowledge by a large model layer by layer to generate a triple, and performing multi-dimensional quality evaluation and dynamic threshold filtering; entity fusion is realized by adopting hybrid alignment of semantic embedding and literal similarity and large model discrimination, and an atlas is maintained by combining incremental updating and version management. According to the method, knowledge integrity and accuracy are improved, entity heterogeneous scenes in the material field are adapted, whole-process automation is achieved, and high-quality knowledge support is provided for material research and development and data sharing.

The invention relates to the technical field of data sharing and access control, in particular to a block chain and reputation mechanism-based education data adaptive access control method, which comprises the following steps of: firstly, constructing a historical interaction-based credible reputation mechanism: setting default trust and reputation values for an initial visitor by an attribute mechanism; calculating an initial reputation value by combining data of other fields acquired by an oracle machine, and binding public keys and reputation information of participants, a block chain storage address and a data abstract; a data owner encrypts data by using a ciphertext policy attribute-based encryption technology, formulates a policy containing an access matrix, trust and a reputation threshold in combination with a static attribute and a reputation mechanism, and binds own public key and data information to a key list; when a visitor requests, risks are quantified, attributes and reputation are verified based on related intelligent contracts, and tokens are generated if the standards are reached, so that safe and credible sharing and dynamic control of education data are realized, and the safety and flexibility are improved.

The present application relates to the technical field of computers, and provides a data sharing method, a device, and a system. Shared data and a data protocol set by a data owner are bound for encryption protection, ciphertext structured data and retrieval metadata are integrally digitally signed, an encapsulation signature and a signature public key certificate are bound on the ciphertext structured data and the retrieval metadata, so that the data authenticity can be verified during circulation and sharing of encapsulated data, and confidentiality and integrity protection of the shared data and the data protocol during circulation and sharing are realized. In addition, the encapsulated data is generated by a trusted device trusted by the data owner, a shared agent is trusted by a plurality of participants participating in data sharing, and data is transmitted between the trusted device and the shared agent through a secure transmission channel, so that the security, confidentiality and integrity protection of the shared data and the data protocol during exporting are realized.

The invention relates to the technical field of computers, in particular to a data sharing management system and method for a database, and the system comprises an identity verification module, a permission adaptation and grading approval module, a risk control module and an iteration module, and the method correspondingly executes the functions of the modules. The identity verification guarantees the legality of an access subject through format verification and multi-dimensional hierarchical authentication, and the intensity of the hierarchical authentication is dynamically adjusted along with the data sensitivity; authority management and control execute differentiation logic according to public, internal and confidential three levels, and fine authorization is achieved; the risk control module monitors abnormal operation in real time and performs grading processing; all operations are recorded in a block chain audit log, and the system realizes dynamic iteration through regular verification and historical data optimization. The data sharing security and cooperation efficiency are considered, the risk controllability and the system adaptability are improved, and the multi-scene data sharing requirements such as enterprise cross-department and external cooperation are met.

The invention relates to a sensitive data sharing method and system based on data consanguinity, and belongs to the technical field of data management and sharing. The system comprises four core components including a data consanguinity module, a sensitive word list recognition module, a data desensitization module and a data sharing module, wherein the data consanguinity module dynamically collects and analyzes a full life cycle flow path of data from a source end to an application end, and constructs a consanguinity map of structured storage; the sensitive word list identification module identifies sensitive data according to the sensitive word list and the blood relationship map, and executes risk grading, dynamic monitoring and compliance auditing; the data desensitization module configures a desensitization rule to deform or convert sensitive data; and the data sharing module performs intelligent approval, dynamic authorization and full-link audit on the data sharing request based on the sensitive word list and the desensitization rule. According to the method, the data security sharing efficiency is remarkably improved, and the security goals of data availability and invisibility and no risk in sharing are achieved.

The invention provides a clinical test data security sharing method and device based on a block chain. The method is applied to the technical field of data processing, and comprises the following steps: extracting feature dimensions corresponding to various types of data, and calculating corresponding data sensitivity coefficients; determining a data sensitivity level of the corresponding data category; extracting corresponding associated metadata; selecting a corresponding encryption algorithm to encrypt the data, and generating corresponding encrypted data and a data abstract; uploading the associated metadata, the encrypted data and the data abstract to a block chain node, constructing a clinical test data sharing account book, and recording a timestamp and a node signature of data operation; extracting identity authentication information and historical access records of visitors, and calculating access credibility; judging whether the visitor is allowed to acquire the decryption key or not; after the visitor obtains the encrypted data, the integrity in the data transmission process is verified based on the non-tampering property of the block chain. Therefore, the security of clinical test data sharing based on the block chain is improved.

The invention discloses a homomorphic encryption-based cross-industry health insurance data sharing and privacy protection method, and relates to the technical field of health insurance data security, and the method specifically comprises the steps: standardizing medical and insurance industry core data, quantifying key indexes such as a disease clinical risk value and an occupational basic risk coefficient, achieving the format unification of cross-industry data, and achieving the privacy protection of the cross-industry data; further generating ownership, risk and business labels, carrying out encryption processing on data through homomorphic encryption on the risk labels based on three scenes of data sensitivity and business influence grading, business label binding pricing, underwriting and claim settlement prediction, adding an integrity check code to high-risk data, screening ciphertexts based on the labels, and then executing fusion calculation; the comprehensive risk adjustment coefficient ciphertext is generated, the data integrity is verified, the comprehensive risk adjustment coefficient is obtained after collaborative decryption, the comprehensive risk adjustment coefficient is applied to premium pricing, underwriting conclusion generation and claim risk pre-judgment, and the scientificity and accuracy of health insurance pricing and underwriting decision making are improved.

The invention discloses an emergency rescue integrated data security protection method based on a block chain, and belongs to the technical field of data security protection, and the method comprises the following steps: constructing an end-side cloud cooperative trusted computing architecture, and establishing a trust chain transmission mechanism from a terminal to an edge node through a physical trusted root; a dynamic multi-dimensional credibility measurement model is adopted, the static credibility, the behavior credibility and the collaborative credibility of the equipment are comprehensively evaluated, and the real-time credibility of the equipment is generated in combination with environmental factors; implementing credibility verification based on the behavior measurement index; cross-domain data sharing auditing is realized by using a block chain smart contract, and the full-life-cycle security of data is ensured through a fragmented storage strategy and a self-destruction key design. According to the method, the problems of insufficient security authentication, dynamic trust evaluation and data cross-domain sharing auditing of the multi-source heterogeneous equipment in emergency rescue are solved.

The invention discloses a cross-industry data sharing method and system using a trusted data space, and relates to the technical field of data sharing, and the method comprises the steps: obtaining a cross-industry data dimension relation; obtaining a sharing request, and carrying out dimension migration analysis on the sharing request to obtain a dimension migration path set; according to the dimension migration path set, adaptive reorganization operation is executed on the to-be-shared data, and reorganized shared data is obtained; according to the cross-industry data dimension relationship and the dimension migration path set, performing cross-industry confidence evaluation on the recombined shared data to generate a shared decision result; and performing data sharing on the recombined shared data through a secure channel of the trusted data space. The technical problems that existing cross-industry data sharing is difficult, and data resources are wasted are solved.

The invention discloses a new energy vehicle supply chain data protection method, system and product based on a block chain and zero knowledge proof, and the method comprises the steps: constructing a decentralized, tampering-free and auditable supply chain data sharing platform through a block chain technology; by adopting an intelligent contract, automatic identity verification, production record evidence storage, order scheduling and reputation management are realized; a zero-knowledge proof technology is applied, and compliance verification and fund flow privacy protection are completed on the premise that sensitive information is not leaked; through a dynamic reputation point mechanism, an order allocation strategy is optimized, and the overall resource utilization rate and cooperation efficiency of the supply chain are improved; an encrypted bill system is adopted, and privacy protection and risk control in the supply chain finance process are achieved. The method pays attention to performance, safety, expandability and user privacy protection, and has good industrial application prospect and commercial popularization value.

The invention relates to the technical field of medical information and block chain crossing, in particular to a medical image co-processing system and method based on a block chain value network, and the system comprises a block chain network, a privacy calculation gateway, an intelligent contract module, a hybrid storage module and a communication module. The intelligent contract module is provided with a collaborative annotation sub-module, a verifiable federated learning sub-module and a dynamic value distribution sub-module; the method comprises the steps of data preprocessing and encryption uploading, collaborative labeling and quality verification, verifiable federated learning model training, dynamic value distribution, consensus evidence storage and supervision auditing. Medical data islands are broken through by means of the block chain network, data availability and invisibility are achieved in combination with the privacy computing gateway multi-encryption technology, the contradiction between data sharing and privacy security is solved, accurate contribution and return matching is achieved through the multi-dimensional contribution degree evaluation model and the intelligent contract, and the accuracy of data sharing is improved. The traceability and the compliance are ensured by full-process operation logs and data uplink evidence storage.

The invention relates to the technical field of government affair data sharing, in particular to a government affair data security sharing and real-time interaction method and device, and the method comprises the steps: firstly deploying a government affair MCP Server cluster, carrying out the data calling and processing, then encrypting the data, and finally achieving the standardized packaging and convenient calling of government affair data resources. Compared with the prior art, 70% of cross-department API development workload can be reduced through an MCP standardized interface, the data leakage risk is reduced to 0.01% through an authority approval and data encryption mechanism, and the data analysis delay is reduced to the second level from the minute level through streaming communication.

The invention relates to the technical field of enterprise ESG digital management, in particular to a sustainable management ESG platform. The platform comprises a data access layer, a twin modeling layer, an intelligent analysis layer, a report generation layer and a collaborative interaction layer. The data access layer is used for standardized acquisition, cleaning and block chain evidence storage of multi-source ESG data; the twinborn modeling layer constructs enterprise ESG digital twinborn bodies through index mapping and scene simulation; the intelligent analysis layer realizes index dynamic monitoring, abnormity early warning and risk quantitative evaluation based on time sequence prediction and causal reasoning; the report generation layer outputs a compliance report through a multi-template automatic adaptation and natural language generation technology; and the collaborative interaction layer realizes multi-role data sharing, task tracking and strategy optimization. According to the method, multi-modal data fusion and an intelligent algorithm are fused, the efficiency of ESG data integration, risk response, report generation and collaborative management is comprehensively improved, and the problems of data isolation, response lag and manual dependence in a traditional mode are effectively solved.

The invention relates to a vehicle-road cooperation data accurate replication method and system. The vehicle-road cooperation data accurate replication method comprises a data extraction step, a data fusion step, a data replication step and a data sharing step. In the data extraction step, original road side data and original vehicle end data are obtained, beyond visual range information data are issued to a road side, vehicle-road cooperative early warning information is issued to a vehicle, and the original road side data and the original vehicle end data are subjected to standardization processing. In the data fusion step, standardized road side data and vehicle end data are associated and integrated to obtain fused sensing data, and scene calculation is carried out by using the sensing data. In the data copying step, original vehicle end data, original road side data, fused sensing data and a scenarized calculation result are copied. In the data sharing step, internal and external data transmission is carried out. According to the method, the problem of multi-source data space-time dislocation is solved, the intersection early warning response delay is shortened, and the decision reliability is remarkably improved.

The invention provides an intelligent processing method, system, equipment and medium for high-risk and newly-onset definite diagnosis crowds of hypertension, and belongs to the technical field of hypertension detection.The method comprises the steps that patient examination data are collected and serve as an original data set; preprocessing the original data; constructing a corresponding parameter group; mapping the family history score into a risk quantification function through a dynamic weight coefficient Wi; summing the product of the dynamic weight coefficient Wi and the quantization function Fi to obtain a total risk score; determining whether the patient is a high-risk population based on the diagnosis code and the total risk score; and pushing the high-risk crowd data to a management center server. According to the method, the data is accurately pushed to a corresponding platform or a storage library, so that safe and effective utilization of the data is guaranteed, and sharing and collaboration of regional medical data are realized.

The invention relates to the technical field of track data privacy protection, and discloses a track privacy data protection and sharing method, which comprises the following steps: carrying out sensitivity grading according to track data and historical access information thereof; identifying the behavior of each access user through an event trigger, and determining a risk value of each access user according to an identification result; and dynamically adjusting differential privacy parameters of the track data according to the risk value of each access user and the sensitivity of the access track data. According to the method, sensitivity grading is carried out on the track data, meanwhile, the risk state of the access user is judged, the risk value is obtained, and track data privacy protection in each data sharing access process is dynamically adjusted through sensitivity grading and the risk value of each access user, so that track data privacy protection is realized; and the privacy protection and sharing requirements of the trajectory data are balanced.