279 results about "Data provider" patented technology

The embodiment of the invention provides a credit scoring method and device for a credit white user. Wherein the data provider comprises a government affair platform party, a transaction opposite party and a financial institution party, and the user data of the financial institution party comprises a user identifier of a credit white user. And the plurality of data providers align the local user data of each party through a privacy intersection algorithm, so that each party determines the first user data of the aligned credit white user from the local user data. And the plurality of data providers respectively encrypt the local first user data of each party and send the encrypted first user data to the service platform. And the service platform performs secret state model prediction based on a local credit scoring model and the first encrypted user data of each party, obtains a secret state credit score of the credit white user, and provides the secret state credit score to a score user. And the score user decrypts the credit score from the secret state credit score, and determines a risk control decision for the credit white user based on the credit score.

The invention discloses a data security sharing method and system, and relates to the technical field of data sharing. The method comprises the steps that a data provider uploads original data, a privacy budget value is calculated through a differential privacy algorithm, noise is added to obtain noisy data, and the noisy data is processed through an anonymous privacy protection algorithm based on maximum dissimilarity degree clustering to obtain desensitized data; encrypting the desensitized data by adopting a block chain decentralization-based ciphertext policy attribute-based encryption method, obtaining an encrypted data ciphertext, uploading the encrypted data ciphertext to a cloud server, and obtaining a content addressing hash value; encrypting the hash value through an elliptic curve encryption algorithm, and storing the encrypted hash value to a block chain account book; and when an access request of a data requester is received, the cloud server verifies the authority by using a non-interactive zero-knowledge proof protocol and returns an encrypted hash value after passing the verification, and the requester decrypts to obtain the hash value and decrypts the encrypted ciphertext to obtain desensitized data, thereby realizing data sharing.

The invention discloses a trusted data sharing method based on a block chain consensus mechanism. The method comprises the following steps: S1, constructing an alliance chain network; s2, completing KZG polynomial commitment generation, ML-KEM algorithm encryption storage and pointer registration; s3, the data provider medical institution completes an evidence storage transaction of the FROST threshold signature; s4, the target medical institution completes the access request transaction; s5, the data provider medical institution sends a KZG unpacking proof and a data item, the target medical institution obtains a storage pointer and a commitment value, KZG verification check is executed, and after verification is passed, an ML-KEM algorithm is executed for depacking to obtain a session key; and S6, the target medical institution completes the audit transaction, and after the alliance chain network broadcast, the consensus node executes rule verification of the access control contract and the audit contract. The multi-node electronic medical record sharing and tamper-proof evidence storage method is high in performance and traceability, and is suitable for credible circulation of sensitive health data among multiple medical institutions.

The invention provides a data capsule generation and verification method and device, equipment and a medium. The method is applied to a data sending end, and comprises the following steps: determining an encryption key and an access strategy corresponding to a data provider; performing encryption processing on the to-be-encrypted data based on the encryption key to obtain encrypted data, and determining a first encryption parameter corresponding to the encrypted data according to the encrypted data and an access policy; based on the first encryption parameter and the identity information, adopting a preset calculation rule to generate a capsule identifier corresponding to the to-be-encrypted data; and packaging the capsule identifier, the access strategy, the encrypted data and a preset calculation rule to obtain a corresponding data capsule. According to the scheme, the verifiable and non-tampering capsule identifier is generated based on the encrypted data, the access strategy and the identity information, so that the risks of data packet switching, content replacement, strategy tampering and the like are effectively prevented, and the structural security and the use control credibility of the data capsule are improved.

The invention discloses a data use control method and system based on a smart contract, and relates to the technical field of data use control, and the system comprises an identity authentication module, a data classification and grading module, a data sandbox module and an evidence storage traceability module. Performing identity authentication on a user request of a data user based on an authentication protocol; performing classification rating on the accessed data; the data user and the data provider negotiate a contract signing detailed rule; the data user and the data provider sign a block chain smart contract; deploying the intelligent contract to a data sandbox, performing authority evaluation and distribution on the user, and performing behavior monitoring on the user; when the data user violates the signed smart contract, terminating the data access permission of the user, and calling the data sandbox to execute operation according to the contract signing content; logs are automatically generated, data access and use circulation are traced in the whole process, and it is ensured that data can be traced, audited and managed from authorization to use. According to the invention, the security and credibility of data circulation are guaranteed in a data transmission use control mode with decentration and multi-party cooperative participation.

The invention discloses a data processing method and device, a medium, equipment and a product. According to the scheme, after the data platform receives the data processing request sent by the data demander, the encrypted first privacy data set can be determined according to the data processing request, and the privacy data set can be processed based on the user data adopted by at least one data provider. The method comprises the following steps: acquiring a first private data set, acquiring a second private data set encrypted by adopting an encryption mode of encrypting the first private data set, comparing the first private data set with the second private data set to obtain an intersection data set, and further returning a service result for a specified service to a data demander based on the obtained intersection data set.

Provided in the present disclosure are a data circulation method and apparatus, and a first platform and a first node. The method comprises: a first platform receiving information which is related to a data resource and is sent by a first node; and the first platform adding to a global data directory the information related to the data resource, wherein the first node is a trusted computing node of a data provider, and the first platform is a data scheduling center platform.

The invention provides an exception handling method and device, a storage medium, electronic equipment and a product. The exception handling method is applied to a credit investigation system corresponding to a credit investigation mechanism, and comprises the following steps: receiving a data query request for target data sent by a data demand side, and querying the target data from a data provider according to the data query request; processing the target data returned by the data provider to obtain a processing result; if the data processing result is successful, credit investigation data obtained after the target data is processed is returned to the data demander; if the data processing result is failure, determining a failure type causing data processing failure based on an abnormal state code carried in the data processing result, and triggering an exception processing flow matched with the failure type; wherein different abnormal state codes correspond to different failure types, and the failure types comprise service side failures caused by inconformity with service rules and system side failures caused by abnormity of the credit investigation system.

A computer-implemented method and a distributed computer system for privacy-preserving distributed training of a global neural network model on distributed datasets. The system has data providers communicatively coupled with each having a respective local training dataset and a vector of output labels for training the global model. Further, it has a cryptographic distributed secret key and a corresponding collective cryptographic public key of a multiparty fully homomorphic encryption scheme, with the weights of the global model being encrypted with the collective public key. Each data provider computes and aggregates, for each layer of the global model, encrypted local gradients using the respective local training dataset and output labels, with forward pass and backpropagation using stochastic gradient descent. One data provider homomorphically combines the current local gradients of the data providers into combined local gradients, and updates the weights of the current global model based on the combined local gradients.

A method, a system, and computer program product for managing tokenized biospecimen transfers are provided. A file including private data associated with one or more donors is retrieved by a biobank node connected to a blockchain network and from a database of a data provider. The file is processed to generate tokens corresponding to the private data, each token representing a biospecimen and being assigned to a respective donor. An inventory is updated by adding identifiers of the plurality of tokens to the inventory. A request to transfer one or more tokens corresponding to a portion of the private data is received. An authorization to transfer the one or more tokens to a target entity is received. The token is transferred to the target entity with a consent to perform one or more operations using the token.

The invention provides a regression test method and device, a storage medium, electronic equipment and a product. The regression test method comprises the following steps: acquiring credit investigation service data generated by a credit investigation system in a process of executing an actual credit investigation service, the credit investigation service data comprises input request parameters input by the data demander to the credit investigation system, original data returned by the data provider to the credit investigation system, actual output request parameters sent by the credit investigation system to the data provider and actual credit investigation data returned by the credit investigation system to the data demander; after business change is executed on the credit investigation system, inputting the input request parameter into the credit investigation system to obtain an analog output request parameter, and inputting the original data into the credit investigation system to obtain analog credit investigation data; and according to the deviation between the actual output request parameter and the analog output request parameter and the deviation between the actual credit investigation data and the analog credit investigation data, determining whether the credit investigation system successfully completes the business change.

The invention relates to the technical field of data circulation and data security, and discloses a digital ecological data market circulation system, comprising: a data provider node deployed with a federated learning client and used for locally training a model by using original data, generating an encrypted intermediate result and managing a corresponding data asset voucher; the data demand side node is deployed with a federated learning coordination server and is used for defining a federated learning task, distributing an initial model and aggregating intermediate results from each data provider node to update a global model; the data element circulation method comprises the following steps of data capitalization and voucher generation, federal learning and process evidence storage, under-chain credible contribution degree evaluation and intelligent contract driven settlement. Compared with the prior art, the method has the advantages that the leakage risk is completely eradicated from the root of technology, a dynamic and accurate value evaluation and distribution system is achieved, a credible environment which is traceable in the whole process and transparent in rule is constructed, and the mobility and financial attributes of data assets are improved.

The invention discloses a privacy protection fine tuning and security testing method for a large model, and belongs to the technical field of artificial intelligence, and the method comprises the steps: 1, enabling a data provider to cooperate with an edge coordinator to generate a feature extraction edge auxiliary model based on edge-end federal pre-training, and carrying out the feature matching of distillation privacy data based on multiple spatial data; 2, the data provider generates distillation data with an invisible backdoor; and step 3, the task initiator finely adjusts the cloud target large model and performs a security test. The method aims at protecting the privacy of private data of the data provider, reducing the scale of fine adjustment data and realizing the security test of the fine adjustment process of the large model.

The invention relates to the technical field of data security, and provides a decision generation method and device, electronic equipment and a storage medium. The method comprises the following steps: receiving a data request comprising request related information, and evaluating whether the request related information meets a compiling strategy to obtain an evaluation result; when the evaluation result represents that the request related information meets the compiling strategy, generating an allowable decision; the compiling strategy is obtained by compiling an abstract syntax tree, the abstract syntax tree is obtained by analyzing strategy description defined by the data provider, and the strategy description comprises a use rule of the data resource corresponding to the data resource identifier. According to the method, the strategy description obtained by describing the use rule of the data resource by the data provider is converted into the abstract syntax tree, then the abstract syntax tree is compiled to obtain the compilation strategy, and then the request related information is evaluated based on the compilation strategy, so that the execution process of the compilation strategy generated based on the abstract syntax tree is simplified, and the user experience is improved. Therefore, the decision generation efficiency is improved.

The invention relates to a data acquisition method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining current collection data item information through a target interface; generating collection configuration information based on the current collection data item information and a preset template, accessing an endpoint of the data agent collection tool according to the collection configuration information, and obtaining an index name and label data in a target format through the endpoint; the data agent acquisition tool determines a data provider by reading the acquisition configuration information, acquires to-be-processed data from the data provider, screens out target data from the to-be-processed data based on the acquisition configuration information, and converts the target data into an index name and label data in a target format; and sending the index name of the target format and the label data to a target database to complete data acquisition. By adopting the method, the efficiency of data acquisition configuration can be improved, so that the flexibility and expandability of data acquisition configuration are improved.

The invention discloses a medical data sharing transaction method based on trust and credibility in a block chain environment, and the method comprises two stages and four roles, and employs four intelligent contracts. The two stages are a request stage and a data transaction stage, and the four roles are a data request user DR, a block chain network, a cloud service CS and a data provider DP; the four intelligent contracts are a data request contract DRC, a recommendation management contract RMC, an attribute management contract AMC and a data sharing traceability contract DSTC; according to the EHR sharing method based on profit driving, the data transaction volume is converted into cost and profit earnings based on the credibility of the DR and the global trust of the DP, and an EHR data credible sharing platform based on trust-driven profit is constructed; a comprehensive trust calculation method of the DP is designed, a trust management model based on global trust is constructed, and profits and earnings of the DP are quantified based on the global trust; normal and malicious DRs are identified through a credibility calculation method of the DRs, and the sharing cost of the DRs is quantified based on credibility.

A data transaction system and method based on data empowerment, the system comprises an identity authentication module for receiving input of the identity information of a provider or a demander, and obtaining a score matched with the identity information of the provider or the demander; a provider module for registering data matched with the score of the data provider according to the score of the data provider; a demander module for calling data matched with the score of the data demander according to the score of the data demander, and generating a transaction request according to selected to-be-transacted data; a management platform for storing registered data and user information, classifying and grading the stored data, scoring the user information, and feeding back the score matched with the provider or the demander to the identity authentication module; and a transaction module for auditing the transaction request and the information of the transaction parties, and sending the demander information and the transaction request to the provider after the audit is passed, and completing the data transaction and empowerment after the provider is verified.

Provided herein are systems and methods for configuring data recovery. A method includes decoding, by at least one hardware processor, a request to recover historical table data. The request is received from an account of a data provider. The historical table data includes a plurality of partition files. Each of the plurality of partition files includes a deleted file designation. Based on the request, a recovery process of the plurality of partition files is performed to obtain recovered partition files. A schema associated with the historical table data is retrieved. Metadata corresponding to the retrieved schema is generated. The metadata is associated with the recovered partition files to recover the historical table data.

Method for selecting source data for generating a response. The invention relates to a method for selecting at least one source data item made available by a data provider (Fourn), said source data being suitable for generating a response to a request from a client agent (Agt) associated with a large language model (LLM). The method comprises: - receiving (E30) from the client agent (Agt) a request and a certificate associated with a statement from the client agent regarding the request, - calculating (E40) a vector representing the received request and statement, - selecting (E40) the at least one source data item by comparing a vector representing the source data, used for generating a response to the request and also representing a constraint on the use of the source data, with the calculated vector. Abstract figure: Figure 1

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 data transmission method and device, equipment, a medium and a program product, relates to the technical field of data security, and is used for ensuring the security of data in a transmission process. According to the specific technical scheme, a first encrypted data capsule from a data provider is received through an isolation container; the first encrypted data capsule is associated with first decryption strategy information; under the condition that the first decryption strategy information is met through verification of the isolation container, the first encrypted data capsule is decrypted through the isolation container based on the decryption key, and target data is obtained; processing the target data through the isolation container, and encrypting and packaging the processed target data into a second encrypted data capsule; sending the second encrypted data capsule to the data user through the isolation container; the second encrypted data capsule is associated with second decryption strategy information. The method and the device are applied to a high-security-level data value circulation and cooperative computing scene.

An approach is provided for signature-based machine unlearning. The approach involves, for example, configuring a machine learning model to learn at least one main task and an auxiliary task. The auxiliary task maps at least one signature associated with at least one data provider to at least one identifier associated with the at least one data provider, and the machine learning model is trained using training data labeled with the at least one signature. The approach also involves calculating at least one data structure representing a sensitivity of at least one parameter of the machine learning model to the training data associated with the least one data provider. The approach further involves updating one or more model parameters of the machine learning model based on the at least one data structure to perform a machine unlearning of the training data associated with the least one data provider indicated in an unlearning request.

The invention provides an information interaction method and device based on a block chain, equipment, a storage medium and a program product, and can be applied to the technical field of block chains. The block chain-based information interaction method comprises the following steps: in response to a data acquisition request initiated by a request initiator, acquiring basic attribute information and dynamic attribute information for a data requester by a data provider; the data provider generates first verification information based on the basic attribute information, generates second verification information based on the dynamic attribute information, and uploads the first verification information and the second verification information to the block chain; calling an intelligent contract by the data demand side, and generating a target credit score for evaluating the credit degree of the user according to the first verification information and the second verification information; and the data demander generates a credit evaluation strategy corresponding to the user based on the credit score.

The embodiment of the invention discloses a data sandbox charging settlement method, system and device, and the method comprises the steps: a data provider and a data user cooperatively generate a data sandbox desensitization rule, a use control strategy and a charging rule, and carry out the uplink; the data user submits a data sandbox use application to the block chain and pays the token, and the block chain allocates a resource use limit to the data user according to a charging rule and the value of the paid token; a data user initiates a data use request, and a data provider inquires a data sandbox desensitization rule, a use control strategy and a charging rule from a block chain every time the data provider receives the data use request, and performs verification according to the use control strategy, the charging rule and a resource use limit; when the verification is passed, querying according to the data sandbox desensitization rule and the data use request to obtain target data, and returning the target data; otherwise, returning the failure reason. And the data security and the charging and settlement efficiency are improved.

Systems and methods are disclosed to provide routing data associated with a destination phone number to a service provider. The service provider sends a first query to a first computing node of a data provider for routing data associated with a phone number. The first computing node provides the routing data if it is available from the data provider. Otherwise, the first computing node identifies a second data provider that is capable of providing the routing data. The first computing node then obtains the routing data from the second data provider and returns it to the service provider.

The invention discloses a photovoltaic power generation power prediction method based on a block chain, and relates to the technical field of block chains, and the method comprises a data provider system, a block chain system, a server system and a prediction model system. According to the method, a data provider system and a prediction model system are separated, a local model unit is introduced, it is ensured that original power generation data do not need to leave the local, model training is only carried out locally, the risk of data leakage and abuse is fundamentally eradicated, and meanwhile, by uploading model parameters instead of the original data, the real-time performance of the system is improved. The method achieves the safe utilization of data values, carries out the non-tampering evidence storage of the hash values of key data and results through the introduction of a block chain system, can achieve the comparison and verification of a server through a result verification unit, and achieves the conversion of a prediction result from the black box output to a white box output. And the credibility and the acceptability of the prediction result in severe scenes such as power transaction and power grid dispatching are improved.

The invention relates to the technical field of privacy computing and big data processing, and discloses a distributed privacy computing big data processing system and method.The system comprises an edge computing layer, a privacy computing layer and a block chain layer, and the edge computing layer is deployed in the local of each data provider and comprises a data cleaning module and a dynamic desensitization module; the privacy calculation layer comprises a federal learning module, a homomorphic encryption module and a resource scheduling module, and the block chain layer comprises a right confirmation module, an auditing module and a hierarchical evidence storage module. Through deep fusion of federated learning, homomorphic encryption, block chain and other technologies, a three-layer architecture distributed privacy computing system of'edge computing layer-privacy computing layer-block chain layer 'is constructed, a corresponding computing method is provided, and the problems of efficiency, safety and auditing of existing privacy computing in big data processing are solved.

A method is provided for training a machine learning ML model for monitoring people based on a data feed capable of describing people. The method is performed by a training data provider (i). The method includes: obtaining (40) a data feed capable of describing people; selecting (42) anonymization levels from a plurality of anonymization levels; anonymizing (44) the data feed according to the selected anonymization level to obtain a processed data feed; and sending (47) the processed data feed as training data for training a central ML model in a central node. Different anonymization levels are available and changes to the levels can be requested by the central node.

Systems and methods are disclosed to provide routing data associated with a destination phone number to a service provider. The service provider sends a first query to a first computing node of a data provider for routing data associated with a phone number. The first computing node provides the routing data if it is available from the data provider. Otherwise, the first computing node identifies a second data provider that is capable of providing the routing data. The first computing node then obtains the routing data from the second data provider and returns it to the service provider.

A method for real-time data communication, an electronic device and a system are provided. A method for real-time data communication executed at an electronic device of a data receiver includes: publishing a resource pool contract to a blockchain network, the resource pool contract is configured to trigger an establishment of a resource pool including a first number of resources; establishing a data flow with each data provider corresponding to a second number of resources in the resource pool, the second number is less than the first number; and receiving real-time data transmitted by the data provider through the data flow.