6262 results about "Privacy protection" patented technology

The invention discloses a data center intelligent operation and maintenance method and system based on an industrial internet of things, belongs to the field of image processing calculation, and aims to solve the problems of privacy protection deficiency, serious manual dependence and weak energy efficiency and safety. Full-link dynamic optimization is realized through an edge calculation layer and an adaptive clustering network, edge nodes are integrated with a wavelet transform noise filtering technology, dynamic denoising is performed on multi-band sensor signals of temperature, vibration, current and the like, heterogeneous device time sequence data are aligned in combination with a dynamic time warping algorithm, sampling frequency differences of multiple devices are eliminated, and the multi-band sensor signals are obtained. A weight formula is put forward, a cluster head is dynamically elected based on node residual energy, data similarity and a load coefficient, a transmission path of a high-delay area is optimized in combination with a multi-hop routing protocol, the packet loss rate and the signal strength are evaluated in real time, redundant coding is started for a high-packet-loss link or the high-packet-loss link is switched to a low-power-consumption mode. And synchronizing the dynamic topology state with the digital twin platform through an open platform communication unified architecture protocol.

The present invention provides a facial recognition system using deep learning methodologies while integrating privacy-preserving capabilities. This system employs convolutional neural networks (CNNs) to extract and classify facial features, ensuring high accuracy in recognition tasks. Moreover, the system addresses privacy concerns by incorporating techniques such as facial feature encryption and anonymization, thereby enhancing user privacy and data security. This invention is applicable across various domains, including security, surveillance, access control, and personalized services, where facial recognition is utilized while preserving individual privacy.

Techniques and systems for the dynamic generation of a map interface include generating temporal activity models by representing individual social media postings as having respective density distributions in time. Each posting's temporal density distribution spans multiple sequential time windows centered on the posting's timestamp, with density contributions decreasing in value for time windows further from the timestamp. The temporal models may be combined with spatial density distributions to generate comprehensive geo-temporal representations of social media activity. A graphical user interface displays an interactive map with overlay elements determined based on calculated activity attributes, including detected temporal patterns and anomalies identified by comparing current activity models against historical baselines. The modeling approach enables improved visualization of activity patterns while providing inherent privacy protection through probabilistic representation of individual posts.

The invention belongs to the technical field of intelligent learning systems, and particularly relates to a generative and multi-modal perception integrated intelligent agent learning system, which comprises the following steps of: establishing a dynamic knowledge graph of a learner by collecting various daily learning data, physiological indexes and basic data of the learner; the teaching strategy planning module is used for establishing a teaching strategy plan of a learner based on a dynamic knowledge graph of the learner, evaluating a specified index of the learner and a specified index of the system after the learner executes a specified time period based on the teaching strategy plan, and optimizing the teaching strategy planning module based on an evaluation result of the specified index of the multi-scale evaluation module. The teaching strategy planning of the learner is optimized, and the modules are coordinated and optimized based on the evaluation result of the specified index of the system. The technical problems that an online education system in the prior art is low in learning efficiency, insufficient in suggestion correlation, not integrated with a teaching feedback mechanism, incapable of reflecting real engineering capability and lack of a privacy protection mechanism are solved.

The invention provides a time sequence knowledge graph federation collaborative optimization method, system and device based on causal inference and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an enhanced knowledge unit with a causal mark through the real-time access of a multi-field heterogeneous data stream and the execution of a space-time alignment operation; by calculating new and old knowledge conflict scores, conflict resolution and version management are realized through a decision tree mechanism, and a time sequence knowledge graph with history tracing is output; node weights are dynamically distributed among distributed nodes based on knowledge entropy, a hierarchical aggregation strategy is adopted to update an entity embedding layer and a relation prediction layer, and a global optimization model is output; the method comprises the following steps: analyzing a natural language query containing an anti-fact condition, extracting a factor sub-graph from a time sequence knowledge graph, executing intervention calculation, and generating an anti-fact influence report, thereby solving the technical problems of a traditional time sequence knowledge graph in the aspects of multi-source heterogeneous data fusion, knowledge conflict resolution and privacy protection; and the accuracy and the interpretability of the knowledge graph are improved.

The invention provides a multi-dimensional data authority management and privacy protection method for an electric power information network, and the method comprises the steps: dynamically selecting the type of a watermark according to the leakage risk level of a screenshot event, and enabling the watermark type to comprise a simple character watermark, a character and image mixed watermark, an invisible watermark, and a two-dimensional code watermark; user screenshot content factors are added in a screenshot event distribution curve of user historical data to judge the risk level of a user, the risk level of a user group is updated, and when the screenshot frequency, time interval, screenshot mode, distribution mode and curve volatility of the user are all normal and screenshot content only aims at sensitive data screenshot, the risk level is improved; a screen capture auditing and monitoring mechanism is established, full-life-cycle recording and tracking are carried out on screen capture behaviors of a user, and screen capture events, watermark embedding conditions and abnormal behavior information in a system are monitored in real time through a visual instrument panel and a report.

The invention relates to a multi-modal public opinion risk early warning system and method based on a dynamic knowledge graph and federal reinforcement learning, and belongs to the technical field of public opinion analysis. The system comprises a data acquisition module, a modal fusion module, a knowledge graph construction module, a comparative learning module, a federal reinforcement learning modeling module and a response output module. The system is based on multi-source heterogeneous data, multi-modal semantic alignment of texts, images, videos and the like is achieved, entity relations and propagation paths are mined through a dynamically updated knowledge graph, collaborative modeling under privacy protection among terminals is achieved by fusing federal reinforcement learning, and then real-time sensing, level early warning and multi-level response strategy recommendation of public opinion risks are achieved. Based on the system, the method has the advantages of high fusion precision, high response speed and strong visual propagation path, and is widely applied to the fields of enterprise crisis management, government affair and public opinion monitoring and public safety.

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

The invention discloses a knowledge graph construction method and system based on traditional Chinese medicine classics, and relates to the technical field of knowledge graph construction, and the method comprises the steps: collecting multi-source heterogeneous data of traditional Chinese medicine classics, carrying out the standardization processing, and generating a cross-time aligned standardized corpus through optical character recognition and multi-modal feature extraction; based on a language model and a causal reasoning engine, extracting an entity relationship and a dialectical rule from the standardized corpus, and generating a dynamic dialectical reasoning model supporting nonlinear reasoning; obtaining an output result of the dynamic dialectical reasoning model, and constructing a redundancy-removed and dynamically-calibrated traditional Chinese medicine knowledge base through knowledge fusion and conflict resolution of various traditional Chinese medicine classics; based on edge calculation and privacy protection technologies, real-time iteration and clinical curative effect feedback optimization of the knowledge graph are realized. According to the method, the problems of trans-era evolution, regional expression difference, multi-modal feature splitting and the like in the traditional Chinese medicine classics digitalization process are solved.

The invention provides a logistics support dynamic scheduling and emergency response system based on intelligent data fusion, and relates to the technical field of logistics support. Comprising a data acquisition module, a data fusion and processing module, a scene prediction and simulation module, a privacy protection collaborative scheduling module, a game optimization scheduling module, a transparent resource tracking module, a self-adaptive network optimization module and a moral priority scheduling module, according to the logistics resource dynamic scheduling system, global efficient optimization of logistics resource dynamic scheduling and intelligent analysis and strategy optimization of real-time data are realized through advanced technologies such as a variable component optimization algorithm, fusion of quantum annealing and a genetic algorithm, deep reinforcement learning and combination of Transform and a genetic algorithm; and the rationality of resource allocation and the scheduling efficiency are greatly improved.

The invention discloses a human resource management method and system based on data security. The method comprises the steps that multi-source human resource data are collected through a standardized interface, and sensitive levels are marked in a classified mode; calling a dynamic encryption engine based on the sensitive level and the data type, performing asymmetric encryption on the structured data, and performing hybrid encryption on the unstructured data; a dynamic hierarchical access control strategy is generated in combination with user roles and service scenes, and the permission range is adjusted in real time; laws and regulations such as GDPR and CCPA are analyzed through a compliance rule base, data operation legality is automatically verified, and illegal behaviors are blocked; k-anonymity and differential privacy technologies are adopted to desensitize sensitive data, and privacy protection and data availability balance are ensured; and recording a full-process operation log based on the block chain and generating a non-tampering audit report. The system comprises a data classification acquisition module, a dynamic encryption engine module, an access control engine module and the like. According to the method, the problems of data protection rigidness, compliance response lag and privacy-utility imbalance of a traditional HR system are solved.

A platform for coordinating networks of specialized AI agents that enables secure collaboration through token-based communication and real-time result streaming. The system features a central orchestration engine managing interactions between domain-specific expert agents, with memory management and optional encryption for secure data handling. The platform uses efficient communication protocols for knowledge compression and faster reasoning, while a standardized agent interface system handles security, privacy, and policy requirements. It scales across distributed computing environments to enable complex collaborative tasks like personalized content creation, materials discovery, and drug development while optimizing resource usage and maintaining data privacy.

The invention discloses a multi-modal large model federated training platform and a heterogeneous data alignment algorithm. The algorithm comprises a time sequence data processing module, a text processing module, an embedded generation module, a local federated learning module, a comparative learning alignment module, a global model aggregation module and a privacy protection verification module. The time sequence data processing module generates a time-frequency matrix signal; the text processing module outputs a text feature signal; the embedding generation module generates a time sequence embedding vector and a text embedding vector; the local federal learning module forms a local encryption signal; comparing the learning alignment module and generating a gradient signal; the global model aggregation module processes the gradient signal through a federated average algorithm and forms a parameter update quantity; and the privacy protection verification module generates confusion gradient signals and transmits the confusion gradient signals back to each module for data alignment. According to the multi-modal large model federation training platform and the heterogeneous data alignment algorithm, the problem that effective alignment and fusion of a logging curve and a geological report are difficult due to modal difference and semantic gaps can be solved.

The invention provides a distributed power data asset management privacy protection encryption architecture design method, which comprises the steps of generating a watermark with robustness and imperceptibility by adopting a content-based digital watermarking algorithm according to statistical characteristics of power data assets, and storing a watermark embedding process and an extraction result through a blockchain; a graph-based data model is adopted, the electric power data assets carrying the watermarks serve as nodes, the data flow relation serves as edges, and an electric power data asset consanguinity graph is constructed; for distributed storage of the blood relationship map, a P2P network topology is constructed by adopting a block chain and a distributed hash table, and blood relationship map data is stored in each node of an alliance chain in a distributed manner; and verifying the digital watermark information embedded in all circulation path nodes through which the power data assets pass in the blood relationship map, calculating the similarity between the extracted watermark and the original watermark, calculating the distortion rate, and judging whether the power data assets are tampered or not.

The invention discloses an intelligent legal data processing method and system based on big data, and the method comprises the steps: S1, constructing a legal data collection framework, and carrying out the unified coding and representation of different data forms through a multi-modal fusion technology; s2, performing hierarchical semantic analysis on the cross-domain legal clauses to generate a high-dimensional semantic representation vector with context sensing capability; s3, performing node embedding, association analysis and reasoning optimization on the constructed legal knowledge graph to generate a dynamically updated multi-domain legal association network; s4, mapping and optimization among terms are realized through semantic comparison and multi-level rule verification; s5, performing modeling analysis on the dynamic trend and the potential risk factors of the legal data, and outputting a risk prediction and early warning strategy of the legal event; and S6, improving cross-data-source cooperative computing capability and data analysis efficiency through a distributed optimization strategy. The method has the advantages of high semantic understanding depth, high risk prediction accuracy and high privacy protection and cooperative computing efficiency.

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

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

The invention discloses a real-time health risk prediction method and system based on a dynamic knowledge graph, and relates to the technical field of medical information. The method comprises the following steps: carrying out multi-modal fusion and privacy protection preprocessing on medical and nursing heterogeneous data, and realizing semantic consistency of cross-mechanism data based on an entity alignment method of a cross-modal graph neural network; based on a hierarchical federated learning framework, local model parameters are subjected to hierarchical encryption and aggregation through a secure multi-party computing protocol to generate an initial global model, and prediction distribution of the global model is optimized in combination with knowledge distillation of differential privacy constraints; designing a gradient difference dynamic updating trigger mechanism of noise robustness, smoothing noise interference through a sliding window mean value, and realizing adaptive threshold calibration through linkage model performance verification; and light-weight deployment real-time reasoning is realized based on redundant edge pruning of confidence and 8-bit symmetric quantization. On the premise of protecting data privacy, the real-time performance and accuracy of health risk prediction are remarkably improved, and the method is suitable for a cross-institution medical care collaborative decision-making scene.

The invention relates to the technical field of data transfer and verification, in particular to a business data testing method and system and a storage medium. The method comprises the following steps: acquiring an original service data stream; deploying a supervision submission shadow processing sandbox based on the original business data flow to obtain a data isolation verification environment; configuring an alliance block chain server based on the original service data flow, and constructing a distributed data processing traceability platform; based on the distributed data processing traceability platform, performing data processing proof generation by using the original service data stream to obtain a data processing hash chain; configuring differential privacy protection processor parameters based on the data isolation verification environment to obtain a privacy protection test data generator; a privacy protection test data generator is trained to generate a synthetic test data set. By combining the alliance block chain and the differential privacy technology, a high-credibility, automatic and traceable data testing system is constructed, and the data security, the verification efficiency and the system consistency are remarkably improved.

The invention discloses a trusted data space cross-domain collaborative analysis method based on privacy computing, which comprises the following steps: step 1, trusted data space construction: edge computing nodes are deployed in a plurality of data owners participating in cross-domain collaborative analysis, and each edge computing node is interconnected through a block chain network to form a distributed trusted data space; a strategy engine is configured in the trusted data space, and the strategy engine is used for defining a data use permission, a privacy protection level and a calculation task life cycle management rule; and step 2, dynamically selecting privacy computing protocols, and selecting at least one privacy computing protocol from a pre-configured protocol library according to the data type, privacy protection requirement and computing complexity of the collaborative analysis task. When the scheme is implemented, the data participates in calculation in a ciphertext or desensitization form in the whole process, and the original data is not out of the domain, so that the privacy compliance requirement is met; and data use rules are automatically executed through block chain evidence storage and the intelligent contract, so that malicious node tampering is prevented.

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

The invention discloses a digital asset secure transaction system and method based on a block chain, and belongs to the cross field of block chain technology and financial science and technology. The system adopts a layered architecture design and comprises an intelligent contract execution layer, a distributed account book storage layer and a cross-chain interaction layer. The transaction method comprises the following steps: generating a digital identity certificate based on asymmetric encryption, and realizing privacy protection through zero-knowledge proof; a secure transaction channel is constructed by adopting a multi-signature mechanism, and sensitive data processing is performed in combination with a trusted execution environment; and designing a dynamic fragmentation strategy to optimize the transaction throughput, and establishing an on-chain and off-chain collaborative verification mechanism. The innovation point is that a double-layer verification model combining a verifiable delay function and a threshold signature is provided, and millisecond-level confirmation is realized while the transaction irreversibility is ensured. The system supports multi-chain asset atomic exchange, and interoperability of different block chain networks is realized through a heterogeneous cross-chain gateway. The scheme has the characteristics of high transaction confirmation speed, high privacy protection level and high system expansibility.

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 relates to a traffic flow prediction optimization method based on an intelligent optimization algorithm, which is applied to the technical field of traffic flow prediction. The system comprises a multi-modal data acquisition and fusion module, a hybrid deep learning prediction module, an intelligent parameter optimization module, an integrated learning module, a multi-scale error correction module, a prediction result analysis and visualization module, a distributed learning and privacy protection module and the like. Wherein the hybrid deep learning prediction module adopts an attention mechanism to fuse models such as CNN, Transform and LSTM, and introduces an adaptive error correction mechanism, so that the prediction precision is remarkably improved; the intelligent parameter optimization module accelerates the model search efficiency by using technologies such as quantum computing and swarm intelligence optimization; the integrated learning module and the adversarial training strategy enhance the robustness and generalization of the model; and the multi-scale error correction module adopts a time sequence decomposition and self-adaptive online learning method to improve the accuracy of long-term and short-term prediction.

The invention provides an industrial control network security service security guarantee system based on behavior analysis, which belongs to the technical field of industrial control network security, and comprises a multi-source data fusion acquisition module, a dynamic behavior modeling engine, a federal learning analysis cluster, an attack chain prediction module, a self-adaptive protection strategy executor and a model evolution feedback ring, wherein the multi-source data fusion acquisition module synchronously acquires industrial control network flow (including OPC UA / Modbus / DNP3 protocol analysis), equipment operation logs, user operation behavior fingerprints and physical interface state data, and the physical interface state data comprises electrical characteristic fluctuation monitoring of USB / network interfaces. According to the scheme, through multi-technology fusion and closed-loop design, the problems of static performance, single-dimension analysis defects and response lag of a traditional industrial control security scheme are effectively solved, a comprehensive protection system with dynamic modeling, intelligent decision making, privacy protection and continuous optimization is constructed, and the security and service reliability of an industrial control network are remarkably improved.

The invention relates to the technical field of data processing, and discloses a block chain-based green product authentication traceability system. The system comprises a data acquisition module for acquiring product full-life-cycle green attribute data by using a multi-source sensing device; the block chain evidence storage module is used for hierarchically encrypting the data based on a hybrid hash algorithm to generate a non-tampering account book record; the intelligent contract auditing module automatically generates a green authentication result through a multi-rule verification model; the traceability query module is used for constructing a privacy protection query channel to output a verifiable traceability chain by applying a zero-knowledge proof technology; and the dynamic optimization module is used for adjusting the authentication standard according to the environmental policy change. According to the system, comprehensive acquisition, safe storage, accurate authentication, reliable tracing and standard dynamic optimization of green product data are realized, the authentication credibility and the supervision efficiency are improved, and the development of the green industry is promoted.

The invention relates to a security protection method, system and device for data sharing and exchange and a medium. The method comprises the steps that original data are loaded, sensitive information in the loaded data is recognized, and the original data are classified according to the category of the sensitive information; obtaining user demand information and a corresponding target scene condition, adding noise to the sensitive information through a differential privacy algorithm according to the user demand information and the target scene condition, and generating privacy protection data; transmitting and encrypting the privacy protection data to obtain corresponding encrypted data, transmitting the encrypted data through a corresponding encrypted channel, recording an operation log in a data transmission process, and embedding an invisible watermark to track the use condition of the encrypted data; and analyzing the privacy protection requirement of the target scene condition, dynamically adjusting the privacy protection parameter of the encrypted data according to a preset use specification, and recovering the encrypted data when the life cycle of the encrypted data is ended. The method and the device have the effect of improving the data sharing security.

The invention, which relates to the technical field of network awareness anomaly detection, discloses a network awareness anomaly detection system based on big data, comprising a data acquisition module, a feature fusion module, a map construction module, a model calculation module, a root cause reasoning module, a threshold decision module and a response control module. The data acquisition module receives network flow data, equipment state data and system log data and outputs a standardized feature set; the feature fusion module is connected with the data acquisition module, dynamically calculates a weight coefficient of each data source based on information entropy, performs feature aggregation of privacy protection through a federated learning framework, and outputs a fusion feature vector; the atlas construction module is connected with the feature fusion module, maintains a network equipment node set and a communication edge set in real time, and updates a space-time association atlas according to a topology change event; and the model calculation module is connected with the atlas construction module, extracts topological features through a space-time diagram convolutional network, and updates a detection model based on an incremental learning mechanism.

The invention relates to the technical field of information, and discloses a ciphertext execution exception handling method and system for a privacy computing algorithm, which quantifies the data query sensitivity of each tenant by collecting differential privacy query parameters of a plurality of tenants, and performs time sequence analysis on the sensitivity quantification result by using an ARIMA model to predict the future trend of the tenant, so as to improve the accuracy of the ciphertext execution exception handling of the privacy computing algorithm. The residual error between the actual sensitivity and the predicted sensitivity is calculated to identify abnormal tenants; a sliding window method is adopted to extract statistical features of the abnormal tenants to construct a feature set, and clustering analysis is carried out on the feature set to identify abnormal modes and abnormal fingerprint feature clusters corresponding to the abnormal tenants; and training a model based on the positive and abnormal fingerprint feature clusters of the abnormal tenants, and quantifying the influence degree of each abnormal mode on the privacy protection level of the abnormal tenants by using the model, so as to adjust and execute the privacy budget allocation proportion of each abnormal tenant according to the obtained quantitative analysis result. And therefore, the exception in the ciphertext execution of the privacy calculation algorithm can be effectively processed.

The invention provides an integrated sharing method and platform for health data of old people, and relates to the technical field of data processing.The method comprises the steps that multi-source heterogeneous health data of the old people are accessed, a rule template in a historical grading preprocessing rule base is matched based on data source capacity and data features, and data cleaning and standardization are completed; further identifying a sensitive field based on a medical data standard and an industry privacy specification, generating a sensitive field vector, carrying out sensitive level scoring, and calling a data desensitization strategy library to execute desensitization operations such as de-identification and masking; an access control function is constructed based on user attributes, access context and field sensitivity levels, authorization levels are determined, differential encryption is performed, and authorization views and data tokens are generated for secure access. Through the method, the problems of heterogeneous sources, complex processing, insufficient privacy protection and lack of flexibility of a sharing mechanism of old-age health data in the prior art can be solved.