135 results about "Data availability" patented technology

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 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 an interpretable differential privacy parameter adaptive method for a generative medical record, and relates to the technical field of medical data privacy protection and artificial intelligence. The method comprises the following steps: acquiring an original medical record data set, and identifying a field containing sensitive information; constructing a multi-dimensional evaluation model, and respectively calculating the sensitivity score of each field and the utility score of the downstream task; dynamically allocating differential privacy budget parameters for each field by using a constraint optimization algorithm based on a game relationship between sensitivity and utility; in the training process of the generative model, corresponding noise is injected into a gradient or an input layer according to the distributed parameters; and finally, generating a synthetic medical record and outputting an interpretability report of privacy parameter distribution. By automatically adjusting the DP parameters, the data features of the low-sensitivity and high-value fields are reserved while the high-sensitivity fields are ensured to obtain strong privacy protection, the problem that privacy protection and data availability are difficult to consider in medical data sharing is effectively solved, and a transparent auditing basis is provided.

The invention belongs to the technical field of smart power grid information security, and provides a privacy protection system and method based on smart power grid distributed data aggregation. The method comprises the following steps: constructing a privacy core, a power grid state and a privacy grading data stream of an electric vehicle charging pile network; generating a one-time session blind factor by using the power grid interference characteristics to perform data encryption; channel interference is evaluated based on the power grid state, dynamic verification is carried out on the ciphertext, and interference confidence is generated; executing anti-interference aggregation in a ciphertext state according to the privacy grade and the credibility score; and finally, carrying out differential privacy enhancement on an aggregation result, and outputting security data capable of directly driving power grid services. According to the method, dynamic binding of the secret key and power grid disturbance, elastic fault-tolerant aggregation based on interference perception and differential privacy enhancement fusing data classification and credibility evaluation are realized, so that the problem of balancing privacy protection and data availability in a plug-and-play scene of the electric vehicle is solved.

The invention relates to the technical field of computers, discloses a full-life-cycle big data management method and system for a meteorological engineering project, and aims to solve the problems of data islands, different standards, processing splitting, decision lag and the like in the prior art. The method comprises the following steps: collecting multi-source heterogeneous original data; performing standardization processing to form a data set with unified semantics and space-time reference; classifying and storing according to the full life cycle stage of the project, and constructing a project entity association index; calling a stage adaptation model based on the data set to execute prediction, diagnosis or optimization; generating visual early warning and operation and maintenance suggestions; and pushing a decision instruction to realize closed-loop management. Through adoption of the technical scheme, unified convergence, efficient retrieval, intelligent analysis and closed-loop decision of meteorological engineering project data can be realized, and data availability, early warning timeliness and management automation level are remarkably improved.

The invention discloses a while-drilling electromagnetic forward modeling simulation system and method for a multi-layer non-uniform lossy stratum, and belongs to the technical field of while-drilling electromagnetic forward modeling simulation systems. Comprising a stratum parameter modeling module, an excitation source configuration module, an electromagnetic field component calculation module, a data processing optimization module, a simulation result output module, a model verification calibration module and a multi-scene adaptation module. The three-dimensional model of the multi-layer non-uniform lossy stratum is constructed through the stratum parameter modeling module, the physical characteristics of the complex stratum are accurately adapted, and the limitation that a traditional model can only simulate a uniform or simple stratum is solved. The system completely calculates the components of the three directions phi, r and z of the electric field and the components of the three directions r, z and phi of the magnetic field, and the defect that component calculation of a traditional model is not comprehensive is overcome. Through a combination mode of absolute value conversion, natural logarithm conversion and noise reduction processing, the presentation effect of the electromagnetic field data is optimized, interference factors are eliminated, and the data availability is improved.

The invention discloses a safety production performance evidence storage verification method based on ZKP and multi-dimensional verification. The method comprises the steps of daily data aggregation and commitment generation, zero knowledge proof generation, optimistic summarization and batch uplink, data availability guarantee, time trigger automatic presumption and multi-dimensional verification execution. According to the method, zero-knowledge proof, optimistic summary, data availability sampling, Vickers tree and other frontier cryptography and block chain capacity expansion technologies are fused, and the method has the advantages of low cost, high efficiency and verifiability.

The embodiment of the application provides a cross-organization data interaction method, system and device based on a blockchain smart contract, which can be used in the field of artificial intelligence technology, realizes data security management and control through the blockchain smart contract, reduces the technical capability requirement for a data organization party, guarantees data availability and invisibility, prevents data leakage and abuse, improves data security, does not need to excessively depend on third-party technology, and realizes interaction based on the same simple smart contract development of both parties, realizes equality and mutual trust through the blockchain, does not exist single-party leading and thus generates a security threat to other participating parties, and thus efficiently and intuitively realizes data sharing and circulation.

This invention provides a method, system, device, and medium for data backup of power information communication nodes. The method includes classifying data on the communication node according to preset data classification rules to obtain important and non-important data; generating scan objects based on a preset scan object generator on the communication node and the important data; scanning incremental data on the communication node in real time using a preset scanner on a backup server; performing incremental data backup when the scanner detects the scan object; and deleting the scan object when the incremental data backup is complete. Through features such as data classification, incremental backup, and deletion of scan objects after backup, this invention provides an efficient and reliable data backup solution, effectively protecting important data on power information communication nodes and ensuring data availability and integrity.

The invention discloses an information guide identification and form filling system and method, belongs to the technical field of intelligent information interaction and data processing, and aims to solve the problems of fuzzy demand positioning, fragmentation of information acquisition, low form filling efficiency and insufficient data credibility in traditional information interaction. After initial basic information of a user is collected, a question and answer association graph is constructed through a graph neural network, the initial information is mapped to graph generation node association features, and a guide label is generated through clustering analysis; matching a core guiding main line based on guiding labels and node weights, dividing interaction stages, generating a verbal skill construction guiding plan, starting self-adaptive question and answer to construct an original demand pool, and verifying and integrating the original demand pool into an initial demand information set; and matching a multi-modal acquisition target to generate a standardized data set, correcting structured information to position a target demand, matching a scale template, and realizing intelligent form filling through information quantitative conversion and deletion completion. According to the method, the cross-scene information interaction efficiency and the data availability are remarkably improved.

This invention relates to the field of data monitoring technology, and more particularly to an enterprise data monitoring system based on big data analytics, used to solve the problems of incomplete, inaccurate, and duplicated data from multiple channels. It includes a data acquisition module to determine the data missing rate and data consistency; a data diagnosis module to determine data categories to differentiate data analysis methods; a data analysis module to calculate data anomaly characteristics, classify data anomaly trends to determine data repair methods, or mark usable data; a data repair module, for data with strong anomaly trends, dividing the data into several anomalous text data frames, predicting the anomalous text data frame groups, and verifying the predicted anomalous text data frame groups; for data with weak anomaly trends, using a trained convolutional neural network for repair, determining successfully replaced data; and a data processing module to determine data availability and mark usable data.

The application relates to the field of fault detection and discloses a pipe network fault diagnosis system, which comprises a data acquisition module, a data preprocessing module, a data analysis module and a model development module. The data acquisition module is used for acquiring pipeline operation data from a SCADA system, a simulation environment, an intermediate database, API transmission and manual inspection data input. The data preprocessing module is used for cleaning, normalizing and structurally storing the collected data, including data availability inspection, basic statistical quantity 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 value judgment mechanism. The model development module is used for selecting an optimal model according to data distribution characteristics, supporting multiple model integration, generating and evaluating multiple model results and outputting visualized model results by adopting a hybrid modeling strategy. The real-time early warning module is used for monitoring the operation state of the pipe network in real time and issuing early warning information based on diagnosis results and performing visualized display.

The invention relates to the technical field of information processing, and discloses a data cross-border transmission method and device, computer equipment and a storage medium, and the method comprises the steps: collecting supply chain data to form a multi-modal data set; identifying and marking privacy data by using a pre-trained multi-modal privacy identification model; determining the sensitivity level of each piece of privacy data based on a preset sensitivity label library; matching a corresponding target desensitization strategy from a preset strategy library according to the sensitivity level, wherein desensitization processing of different intensities is adopted for different levels; performing differential desensitization on the privacy data by using the matched strategy to obtain a desensitized data set; and finally compressing and encrypting the data set and transmitting the compressed and encrypted data set to a data requester. According to the method, a refined hierarchical desensitization mechanism is adopted, so that the problem that data availability reduction and compliance risk coexist due to a fixed desensitization strategy in the prior art is solved, and the value of data for model training and analysis is improved while the data outbound compliance requirement is met.

The invention relates to the technical field of power distribution network power supply and operation control, and discloses an intelligent operation management method of a primary and secondary fusion ring main unit. The method is used for solving the problems of safe and stable operation and poor rapid power restoration effect of a power distribution network in a traditional method. The method comprises the following steps: firstly, a service triggers and freezes topology and a strategy version, and a sampling window and an event window are established to gather remote signaling, remote measurement, receipt and alarm to form an operation context; carrying out hierarchical grading on point locations according to freshness, missing and conflicts, and determining a data availability level, a risk level, a decision conservative level and segmentation granularity; isolation locking, sequence protection, transfer boundary reliability and time sequence alignment checking are sequentially completed to generate a constraint list, a plan packet is issued according to segments, segment preconditions, multi-source confirmation points and rollback rules are configured, monitoring confirmation is conducted, rollback is conducted when abnormity occurs, or an archiving track is terminated, and auditing and rapid isolation power restoration can be achieved.

The invention discloses an efficient data availability and invisibility implementation system, which comprises a trusted data client, a network space security center, a router, a big data platform and a block chain system, and is characterized in that the router is connected with the trusted data client, the network space security center and the big data platform, and the big data platform is connected with the block chain system. According to the invention, the data security client is provided to realize the data availability and invisibility function, and the data learning analysis plug-in platform is provided through the data security client, so that rapid fusion of the data security client and a data analysis system of a client can be realized, and the data can be taken to a user side for analysis and learning; therefore, various defects caused by the fact that the data needs to be learned and analyzed at the data provider are avoided.

The invention provides a high-availability distributed cache query method and device, equipment and a storage medium, when a first instance node triggers an update event of index content, the index content in a memory of the first instance node is updated to obtain an update index corresponding to the first instance node, and the cache of redis is subjected to corresponding data version update, so that the update index of the first instance node is obtained. Before updating is completed, the old data version is available, and it is guaranteed that data in the updating process is available; and sending an updating signal based on an updating index corresponding to the first instance node to the redis channel through the first instance node, notifying a second instance node subscribing to the redis channel to update the corresponding updating index, and when a query index is received, querying corresponding query content from the updated data version in the redis according to the corresponding updating index and the query index, the deserialization overhead is reduced, and the query efficiency is improved.

The invention discloses an intelligent networked automobile data security policy arrangement method based on reinforcement learning, and belongs to the field of automobile data security. The method comprises the following steps: determining initial sensitivity levels of various feature data in an automobile data set; for each feature data of the automobile data set, executing the following steps: taking the initial sensitivity level of the current feature data as the state of a Markov decision process, and taking a pre-constructed security policy set as an action to initialize a reinforcement learning algorithm; and constructing a multi-target reward function consisting of a resource consumption function, a sensitive level change, a law and regulation conformity function and a data utility maintenance function, so as to perform security policy arrangement on the current feature data by using a multi-target reinforcement learning algorithm. According to the scheme, the feature data meets the user privacy regulations, waste of computing resources is reduced, the data sensitivity level is reduced, the data availability is improved, and the balance among the sensitivity level, the computing resources and the user privacy regulations is achieved.

The invention discloses a data display method and device for restoring a driving scene. The method comprises the following steps: storing received image data into a double-end cache region through a target client; aiming at each piece of image data under each theme, carrying out frame alignment on each piece of image data under the theme according to the timestamp of each piece of image data to obtain image data after frame alignment; and in response to request information input by a user, rendering and playing the image data. Therefore, multi-source data cloud end and vehicle end dual storage can be realized, and data availability is guaranteed. Meanwhile, the problem that multiple views are not synchronized is solved based on timestamp frame alignment. The driving scene restoration accuracy, the data processing efficiency and the multi-platform applicability are integrally improved, and the user experience is optimized.

The invention discloses a data anonymization processing method and system based on differential privacy, and relates to the field of data anonymization processing, and the method comprises the steps: receiving a data retrieval request of a data user; retrieving from an original database to obtain an initial data set, and constructing a differential privacy parameter search space; randomly determining a first differential privacy parameter in the differential privacy parameter search space, and performing data anonymization processing on the initial data set; performing data privacy evaluation and data availability evaluation on the first to-be-evaluated data set to obtain a first data privacy degree and a first data availability degree, and generating a first parameter fitness; performing parameter optimization based on the first parameter fitness and the first differential privacy parameter, and determining an optimal differential privacy parameter; and performing data anonymization processing on the initial data set to obtain a target data set, and feeding back the target data set to a data user. The problems that in the prior art, data privacy leakage cannot be quantized, reliable privacy guarantee is difficult to provide, and dynamic adaptability of differential privacy parameters is poor are solved.

The application provides a blockchain system privacy protection evaluation method and device, the blockchain system privacy protection evaluation method and device provided by the application, through privacy protection strength analysis, transaction data availability analysis and privacy protection technology performance analysis of the target blockchain system, the privacy protection analysis result of the blockchain system is obtained, the privacy protection evaluation index system of the blockchain is constructed based on the privacy protection analysis result, and the corresponding weight of the privacy protection evaluation index is allocated according to the specific application scene and type of the target blockchain system, and finally the privacy protection ability score is calculated, the quantitative evaluation of the privacy protection ability of the blockchain is realized, the privacy protection ability of the blockchain system is convenient for non-staff to understand, so that the blockchain project meeting the privacy protection standard of the user is selected.

The invention discloses a public institution internal data sharing method based on multi-source data fusion, and relates to the technical field of data sharing. According to the method, four-quadrant accurate division is carried out on multi-source fusion three-medicine structured data on the basis of sensitive risk scores and utility influence tolerance, differential noise adding strategies are matched in a targeted mode, and three kinds of deviation verification of medical quality, medical insurance fund risks and medicine supply and demand results and noise iterative optimization are combined, so that the multi-source fusion three-medicine structured data is obtained. And finally, through block chain intelligent contract encryption sharing, the problems that a traditional differential privacy technology is rigid in noise intensity setting and is difficult to adapt to data of different privacy levels are solved, dynamic balance between privacy protection strength and data availability is realized, meanwhile, the security and credibility of a data sharing process are guaranteed, and the data sharing efficiency is improved. On the premise that the multi-source heterogeneous public institution internal data meets the privacy security requirement, the practical value of supporting core services such as medical quality evaluation, medical insurance supervision and drug supply and demand allocation is reserved to the maximum extent.

The application discloses a kind of unmanned plane identity anonymization and safe data outsourcing method and system, method is based on integrated supervision platform and unmanned plane, including system initialization stage, integrated supervision platform generates main private key and public parameter;User anonymous and private key generation stage, unmanned plane registers anonymous identity and obtains user private key;Data preprocessing and outsourcing stage, unmanned plane divides data into block and sub-block and encrypts, generates coding vector using erasure code coding, generates verification label for each coding data block, constructs random distribution table according to the number of cloud server, distributes coding vector and label to multiple cloud server storage.In this way, the unmanned plane identity anonymization and safe data outsourcing method described reduces storage redundancy, ensures that data can be reconstructed when multiple servers fail, and guarantees data availability and robust recovery.

The invention relates to the technical field of information, in particular to a smart education-oriented federated learning and differential privacy fusion data security processing method, which comprises the following steps that: each education institution performs preprocessing and feature extraction on local education data through edge nodes, and trains a local model; based on gradient importance of local model parameters, dynamically adding noise to realize adaptive differential privacy protection; the central server aggregates the noise-added parameters to generate a global model and issues and updates the global model, and optimization is carried out through combination of multi-round iteration and a dynamic privacy budget strategy; and finally deploying the convergent model in an intelligent education application scene. According to the method, through deep fusion of federated learning and differential privacy, the contradiction between data privacy protection and value mining in intelligent education is solved, data availability and invisibility are realized, and model performance and cross-mechanism cooperation efficiency are remarkably improved on the premise of guaranteeing student privacy.

The application discloses a kind of multi-source consumption behavior personalized recommendation method and system for privacy computing, it is related to data processing technical field, including, using dynamic differential privacy disturbance algorithm, to structured feature vector set is carried out differential privacy disturbance processing, generates differential privacy feature data, and carries out homomorphic encryption, generates encrypted feature dataset;Iterative training is carried out to encrypted feature dataset, generates local encrypted parameter;Local encrypted parameter is transmitted to encryption aggregation center, obtains encrypted parameter set, and using homomorphic encryption aggregation method, encrypted parameter set is merged, generates global encrypted recommendation parameter;Global encrypted recommendation parameter is decrypted in trusted execution environment, obtains available recommendation parameter, and combines local encrypted parameter and differential privacy feature data and carries out recommendation calculation, generates personalized recommendation result.The application improves privacy protection capability and data availability.

The invention relates to a data matching method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of performing semantic analysis on a business query text through a pre-trained large model, and determining a target index; obtaining a parameter collection result of each candidate calculation strategy corresponding to the target index; each candidate calculation strategy is used for calculating a target index according to the corresponding at least one calculation parameter; and based on a parameter acquisition result, determining a target calculation strategy from the candidate calculation strategies, and calculating an index value of the target index through the target calculation strategy and the corresponding calculation parameter. Therefore, the calculation of the target index does not depend on a single calculation mode solidified in the code any more, but can flexibly and intelligently match and execute the most appropriate calculation path according to the actual data availability and service requirements, thereby greatly improving the adaptability and response speed of the system, and improving the system performance. And real-time sensing and dynamic monitoring of energy and carbon data indexes are realized.

The invention relates to the technical field of computers, in particular to a data privacy protection method and device, a storage medium and computer equipment. The method comprises the following steps: receiving a data query request sent by a data request main body; extracting a multi-dimensional risk feature vector of the data query request; based on the multi-dimensional risk feature vector, obtaining a risk score of the data query request through a target risk prediction model corresponding to the subject category to which the data request subject belongs; generating an original query result according to the data query request; and based on the risk score, performing different deformation processing on the original query result, and sending the original query result subjected to the deformation processing to the data request main body. According to the method, the balance between data privacy protection and data availability is realized through the dynamic risk score and different data deformation strategies.

This invention relates to the field of distributed storage technology and provides a data collaboration method and system for distributed transaction terminals. The method involves each transaction terminal performing erasure coding on its local ledger data to generate a set of data fragments consisting of original data fragments and verification data fragments. These data fragments are then distributed and stored across different transaction terminals according to distributed storage rules, and the terminals exchange online status and fragment integrity information. When a request to access the target ledger is received, the terminal storing its fragments is located based on the ledger identifier, and the fragments are retrieved in parallel to reconstruct the target ledger. When a terminal is detected to be offline or a fragment is corrupted, online terminals collaborate to reconstruct the original data using surviving fragments, re-encode new fragments, and distribute them to healthy terminals. This invention, through erasure coding and distributed collaborative storage, reduces storage overhead while improving system fault tolerance and data availability, achieving efficient and reliable distributed ledger data collaborative management.

The application relates to the computer technical field, in particular to a data privacy protection method and device, a storage medium and computer equipment.The method comprises the following steps: receiving a data query request sent by a data request subject; extracting a multi-dimensional risk feature vector of the data query request; obtaining a risk score of the data query request by a target risk prediction model corresponding to a subject category to which the data request subject belongs based on the multi-dimensional risk feature vector; generating an original query result according to the data query request; performing different deformation processing on the original query result based on the risk score, and sending the original query result after the deformation processing to the data request subject.The application balances data privacy protection and data availability through dynamic risk scoring and different data deformation strategies.

The invention discloses a smart park articulated naturality web node data distributed storage system, and relates to the technical field of smart park data storage. Comprising a data acquisition layer, a distributed storage node cluster, a metadata management module, an intelligent scheduling module, a data security guarantee module, a data verification module, a data adjustment module and an application interface layer, and all the modules cooperate to realize efficient storage, quality control and management of articulated naturality web node data. Aiming at a smart park articulated naturality web data storage pain point, efficient storage and management and control are realized through multi-module cooperation, a distributed architecture, mixed storage and intelligent scheduling are adopted, the storage efficiency and the resource utilization rate are improved, multi-copy backup, cross-physical deployment and automatic fault repair are realized, data reliability is guaranteed, and the system is suitable for large-scale popularization and application. The method comprises the steps of full-process encryption, authority control and multi-dimensional verification, building of a data security defense line, data dynamic optimization and standardization of an interface, enhancement of data availability and system compatibility, and adaptation to the dynamic increase demand of park data.

The invention discloses a traffic prediction and anomaly detection method based on sparse spatio-temporal data of an operator base station, and the method comprises the steps: carrying out the restoration of high deficiency and structural deficiency before modeling, and reserving space-time interaction information, thereby remarkably improving the data availability and effective sample density; under the unified representation of fusing a multi-relation network structure and time dynamics, neighborhood association, short-term fluctuation and cross-cycle trends can be described at the same time, so that the accuracy and stability of multi-step prediction are improved; under the condition of less labeling or no labeling, by means of representation learning of bidirectional reconstruction and discrimination consistency, abnormity is synchronously amplified in a data domain and a potential representation domain, and in combination with a threshold value self-adaptive strategy, alarm effectiveness is improved, and false alarm and missing alarm are reduced; and in cooperation with edge-side reasoning and lightweight configuration, the requirements for real-time performance and operability are met, and the problems of sparse spatio-temporal data, complex topological coupling, insufficient anomaly detection robustness and the like are integrally solved.