351 results about "Data availability" patented technology

The invention discloses a user data intelligent protection method and system based on differential privacy, and belongs to the technical field of data protection, and the method specifically comprises the steps: collecting and preprocessing user health data; setting a dynamic privacy budget according to the data sensitivity, the analysis purpose and the privacy demand, and adding random noise to generate disturbance data by adopting a multi-strategy fused data disturbance strategy; statistical analysis is carried out, and an improved differential privacy algorithm is used to assess the privacy leakage risk, the protection degree and the data availability; based on an evaluation result, a privacy budget and a disturbance strategy are iteratively adjusted through a feedback loop mechanism; the health data analysis method provided by the invention not only can protect user privacy, but also can ensure data availability.

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.

Event-triggered forensics capture technologies balance security incident data availability against data processing and storage costs. A forensic correlation engine receives basic status data of a monitored computing system. A forensic computing system detects a trigger event in the basic status data, and starts capturing extended status data per a corresponding capture specification. Captured data is submitted to a forensic analysis tool. Different trigger events may cause different data captures. A query specifying which data to capture from a live stream or from virtual machines may operate as a capture trigger start event. Extended status data capture activity may be stopped by a change in the basic status data being received, by a timeout, or by forensic analysis that finds no vulnerability or threat based on captured data. Data transfers and storage may be restricted to comply with privacy regulations or policies.

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 privacy protection query language PQL and system thereof, comprising PQL statement and system, the system comprises parsing module, query module and noise-injection module; the parsing module comprises lexical analyzer and syntactic analyzer; user inputs PQL statement according to predetermined semantic and syntactic rules, after parsing module receives the PQL statement, lexical analyzer performs error checks on the PQL statement, correct results are sent to syntactic analyzer; the syntactic analyzer performs grammatical and semantic checks on PQL statement, generates a mapping table and a parameter table with correct results; after receiving mapping table, the query module encapsulates the same into SQL statement, and verifies the encapsulated SQL statement; the noise-injection module obtains final query results of the query module, calculates noise injection sensitivity according to parameter table; the invention ensures the data privacy security and satisfies that the injected noise does not affect data availability.

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 a terminal equipment intelligent fault diagnosis method based on electric power internet of things edge computing. The method comprises the following steps: constructing a cloud edge interconnection architecture; in a data acquisition stage, real-time denoising and standardization processing are carried out by utilizing an edge computing layer, and data availability and access speed are guaranteed by adopting an edge caching technology; data from different sensors are integrated by using a data fusion technology, the accuracy of fault detection and diagnosis is improved, multi-source data association analysis is applied, and multi-angle early warning is performed for abnormal behaviors; event mode identification and processing are realized on an edge calculation layer by using CEP, and potential fault signals are quickly responded; all transmission data of the communication layer uses an end-to-end encryption protocol to ensure data security, and edge device intrusion detection and firewall protection are implemented to prevent hostile attacks. According to the invention, the accuracy and efficiency of power equipment fault diagnosis are improved, and the safety and operation stability of the system are remarkably enhanced.

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 data availability verification method and device, equipment and a computer program product, and relates to the technical field of blockchains, and the method comprises the steps: receiving user data sent by a client, carrying out the fragmentation encryption of the user data, obtaining first Merkel data of the user data, and sending the first Merkel data to the client; sending the first Merkel data to a spot check verification end; receiving an encrypted random number sent by the spot check verification end, obtaining a target verification parameter according to the encrypted random number and the first Merkel data, sending the target verification parameter to the spot check verification end, and performing parameter verification on the target verification parameter by the spot check verification end, and obtaining an access verification result of the user data. The access verification result is obtained by encrypting and verifying the user data, so that the problem that the cloud disk data cannot be always online and readable is solved, and the safety of user data storage is improved.

The invention discloses an automatic irrigation control system and method for a tea garden based on photovoltaic power generation, and particularly relates to the technical field of agricultural irrigation, photovoltaic power generation capacity, tea tree growth irrigation cycle and meteorological condition information are collected in real time through multiple types of sensors, and formatting processing is carried out to ensure data availability; information lag degree analysis is carried out based on the processed data, and feedback lag early signs in photovoltaic power generation and tea garden irrigation cooperative control are identified; according to the lag signs, extracting power supply fluctuation, tea tree irrigation demand urgency and climate environment change characteristics, and generating corresponding fluctuation indexes, demand indexes and change indexes; dividing a feedback sampling period into a high-quality period or a low-quality period by using fuzzy logic, and dynamically adjusting the sampling frequency based on the current sampling frequency in the low-quality period; the feedback sampling frequency can be dynamically adjusted, and the stability and the response speed in the cooperative control process of photovoltaic power generation and tea garden automatic irrigation are effectively improved.

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 relates to the technical field of electric energy metering information transmission, and discloses an electric energy metering information transmission system and method. Through data acquisition and preprocessing, voltage, current and power signals are acquired, a normalized time sequence matrix is generated, the feature expression capability is enhanced through nonlinear transformation, and the problem of signal complexity is solved. And feature extraction and compression are combined with sparse processing, key feature information is extracted, the data dimension and redundancy are reduced, the storage and transmission pressure is reduced, and the transmission efficiency is improved. The dynamic transmission strategy adaptively adjusts the frequency and priority based on the network state, and enhances the transmission reliability in combination with a verification mechanism. And in decoding and reconstruction, signal features are recovered by using a reverse reconstruction algorithm, so that the precision and data availability under a high compression ratio are ensured. Data analysis and anomaly detection are combined with high-quality features and a dynamic algorithm, abnormal values and abnormal fluctuations are recognized in real time, and the adaptive capacity of the system in a complex environment is 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 belongs to the technical field of digital management, and provides an intelligent water conservancy construction management platform which comprises a database module, an AI data analysis module and a construction management module. The database module is used for acquiring multi-source hydraulic engineering information; each processing sub-block in the AI data analysis module is used for performing standardization processing on various types of data; the association analysis block is used for performing association analysis on various data to obtain association features; and the construction management module is used for constructing a water conservancy construction site simulation model according to various data and associated features after standardization processing, and obtaining a construction simulation result according to the current construction data. Data association features are mined and model parameters are adaptively adjusted by using a space-time convolutional neural network through association analysis blocks, and a construction management module constructs an accurate model and updates simulation in real time, so that data availability, model accuracy and construction management scientificity and efficiency are improved, resource allocation can be effectively optimized, and construction quality and safety are guaranteed.