133results about How to "Guaranteed privacy and security" patented technology

The invention provides a security management and retrieval method based on Internet of Vehicles data, and the method comprises the steps: carrying out the cooperative training of a global key management model through a federated learning technology on the premise of not leaking the original data of all parties, and achieving a security key management scheme under the participation of multiple parties through a model parameter aggregation and updating mechanism; when data retrieval is needed, a user submits a retrieval request and identity authentication information, and after a system verifies the identity of the user, a ciphertext data identifier meeting conditions is matched in an attribute index according to a keyword and an authorization attribute of the request; the system acquires corresponding ciphertext data from a distributed database according to the ciphertext data identifier, and directly performs keyword matching calculation on the ciphertext data through a secure multi-party computing protocol under the condition of not decrypting the data; and according to the mined security threats and abnormal modes, a data encryption strategy and an access control strategy are dynamically adjusted, authority limitation or access blocking is performed on suspicious users, and the security protection capability of the system is improved.

The invention provides an information management method and system for an access control card chip. The method comprises the following steps: providing an anonymous identifier, initializing an authentication process, and laying a foundation for privacy protection and a data link; mutual trust authentication of the access control card and the equipment is completed, and authentication challenge data is provided for follow-up behavior recording and tracing; based on a result and challenge data generated after authentication is completed, generating a complete encryption behavior record and establishing a chained hash structure; storing the encrypted behavior record and the chained hash value locally or synchronizing the encrypted behavior record and the chained hash value to a server to ensure the reliability and traceability of the behavior record; and detecting abnormal behaviors and supporting traceability verification by using complete behavior chain data. Dynamic identity generation, encryption behavior chain storage and a bidirectional authentication mechanism are integrated in the access control card chip, so that the privacy protection capability, the safety and the tracing efficiency of the access control system are remarkably improved.

The invention provides an intelligent digital image processing system for urinary surgery. The intelligent digital image processing system is suitable for accurate diagnosis and dynamic monitoring of diseases such as kidney tumors and bladder tumors. The system comprises a data acquisition module, a multi-modal image fusion module, an image segmentation module, a dynamic lesion tracking module, a three-dimensional modeling module, an intelligent diagnosis support module and a federal learning module. The data acquisition module receives CT, MRI and other image data; the multi-modal image fusion module generates a high-quality comprehensive image through a registration algorithm; the image segmentation module extracts a lesion area by using deep learning; the dynamic lesion tracking module calculates lesion volume, surface area and change trend thereof; the three-dimensional modeling module generates a three-dimensional visual model of the lesion area; and the intelligent diagnosis support module outputs a diagnosis report containing lesion features, development trends and treatment suggestions. According to the invention, through multi-modal fusion and dynamic monitoring, the accuracy of lesion area extraction is improved.

The invention belongs to the technical field of vehicle dynamics prediction, and discloses a vehicle data prediction method based on GRU and differential privacy federated learning, and the method comprises the following steps: a server side randomly initializes global GRU model parameters, and then carries out the training of a local GRU model; the client processes the trained local GRU model parameters through a differential privacy mechanism to generate a disturbance model; each client uploads the disturbance model parameters to a server side; carrying out global GRU model aggregation by adopting a weighted aggregation algorithm in federated learning; and the server side redistributes the aggregated global GRU model to each client side, and the global GRU model for accurately predicting the vehicle dynamics is obtained through multiple rounds of training and updating. According to the vehicle data prediction method based on GRU and differential privacy federated learning, through combination of a GRU network and a differential privacy mechanism, vehicle dynamics prediction in an Internet of Vehicles environment is realized, and privacy of a user is effectively protected.

The invention provides a commodity service recommendation method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps: receiving a global recommendation model issued by a service terminal; processing the historical interaction behavior data set of the user in the client based on the global recommendation model to obtain an initial candidate recommendation set; a preset cue word template is filled with the initial candidate recommendation set and the historical interaction behavior data set, and recommended cue words are generated; inputting the recommendation prompt words into the large language model to obtain a reordering recommendation candidate set output by the large language model; and performing similarity verification on the initial candidate recommendation set and the reordering recommendation candidate set of each user, screening out unmatched recommendation items in the initial candidate recommendation set, and outputting a service recommendation result. According to the method, distributed collaboration of federal learning and semantic comprehension and generation capability of a large language model are combined, so that accurate commodity service recommendation is realized on the premise of protecting data privacy.

The invention discloses a customer service response evaluation method and system based on a large language model, and relates to the technical field of customer service responsiveness, and the method comprises the steps: obtaining customer service dialogue data, carrying out the emotion score assignment through an emotion dictionary and an emotion label library, and obtaining a customer service emotion text; obtaining a plurality of groups of different emotion threshold combinations, and layering the customer service emotion text to obtain the emotion level of the customer; acquiring emotion matching data and customer emotion change data; constructing an emotion threshold evaluation model based on machine learning; constructing an effect display model, carrying out data analysis on the effect display model to obtain an optimized emotion threshold combination, and applying the optimized emotion threshold combination to customer service response evaluation; according to the method and the device, the problem that the customer satisfaction and the service quality are finally influenced due to inaccurate analysis result and unstable service effect caused by experience-based setting of a sentiment analysis threshold value is solved.

The invention provides a privacy protection method and device for an audio information confusion reversible adversarial sample. The method provides a new framework named as an information confusion reversible adversarial example. According to the framework, the original quality of the audio can be recovered under the condition that authorization is obtained while the privacy security of the audio is ensured. By adopting the method provided by the invention, the security and privacy protection level of the audio data can be greatly improved, and unauthorized access and analysis can be effectively prevented. The objective of the invention is to solve the problems in the prior art that the antagonism attack in the audio data privacy protection is easy to detect, the quality of the processed audio is obviously reduced, and the existing reversible antagonism example (RAE) technology is limited in an audio application scene.

The embodiment of the invention relates to the technical field of computers, in particular to a business interface optimization method, device and equipment based on a multi-dimensional customer group. Comprising the following steps: acquiring behavior data and basic information of a plurality of clients for handling services through a service interface of a target service at a plurality of continuous moments; dividing the plurality of customers into a plurality of predetermined customer groups according to the behavior data and the basic information at each moment to obtain a customer group corresponding to each moment; calculating the customer group conversion rate of each business interface according to the corresponding relationship between the customer group and the business interface and the customer number of the customer group corresponding to each moment; determining a business interface needing to be optimized according to the customer group conversion rate as a business interface to be optimized; reading client feedback information of the to-be-optimized business interface at each moment; and determining an optimization item of the to-be-optimized service interface according to the feedback information. According to the embodiment of the invention, the optimization workload is reduced, and the privacy security of customers is ensured.

The invention discloses a privacy protection feature engineering method and system based on secret sharing, and the method comprises the following steps: S100, in a data set secret distribution stage, n data providers # imgabs0 # separately distribute respective privacy data features and labels # imgabs1 # thereof to a first server S1 and a second server S2 through additive arithmetic secret sharing, obtaining a secret share of the data feature and a secret share of the data tag; and S200, in a two-party distributed operation stage, the first server S1 and the second server S2 respectively perform safe two-party distributed operation in a secret state environment based on the obtained secret share of the data feature and the secret share of the data tag, and the first server S1 and the second server S2 are not colluded. According to the technical scheme, the privacy security of the data can be ensured in any link, meanwhile, the calculation efficiency is considered, and higher expansibility and a complete system are achieved.

The invention relates to the technical field of data privacy protection, and discloses a method, a device and equipment for generating logic real data and a medium, the method comprises the following steps: extracting a sensitive data set from an original data set, and performing grouped hash storage on the original data set according to the sensitive data set to obtain a hash storage bucket set; performing tuple class decomposition on tuples in the hash storage bucket set to obtain a decomposed tuple class set and a residual tuple set; distributing each tuple in the remaining tuple set to each decomposed tuple class in the decomposed tuple class set by using a non-identical random matching method to obtain a standard tuple class set; and carrying out sensitive information disassembly on the standard tuple class set to obtain logic real data. Through iterative multi-component decomposition, different random allocation and sensitive data acquisition implemented by the method, privacy protection of the original data set can be effectively realized, data logic of the original data is reserved, and the efficiency of mining data privacy protection is improved.

The invention relates to the technical field of artificial intelligence, in particular to a client-based financial product recommendation method and device, computer equipment and a medium. According to the method, a configured reference model and a pre-trained local model are used to perform product recommendation on local sample user information, update loss is calculated according to an obtained reference recommendation product and a first prediction value thereof, and a local recommendation product and a second prediction value thereof, and the pre-trained local model is updated according to the update loss, so that the user experience is improved. Product recommendation is carried out on target user information by using the updated local model, and the local model is updated in combination with output of the reference model, so that the updated local model can retain generalization ability of the reference model and retain pertinence of the local model to local data, and therefore, data privacy security is ensured, and meanwhile, user experience is improved. The product recommendation accuracy of the client is improved, and then the financial product recommendation accuracy of financial or insurance business systems based on the local model is improved.

The present invention belongs to the field of information security technology, and provides a privacy measurement algorithm for structured data in a data publishing environment. First, the data source collects user information and generates a user information summary, and filters the external data set based on the summary information and calculates the attribute similarity to distinguish private attributes from non-private attributes. Then, the external and internal average information volume and the distribution information volume of the private attributes are calculated, and the private attributes are priced based on demand and time loss to identify sensitive attributes, and the maximum entropy principle is applied to extract quasi-identifiers and identify risk factors in the data. Finally, the data privacy is measured according to the privacy model, and a privacy-oriented anonymity technology is given, which can prove that the privacy and availability under this orientation have achieved the best balance.

The invention discloses a multi-party key authentication method based on Internet of Things data transmission, and relates to the technical field of communication. The method is based on necessary parameters generated by a key generation center; determining user registration information and equipment registration information on the basis of the biological characteristics of the user and a gateway private key by the key generation center; sending the biological key, the user identity identifier and the first timestamp of the user to a gateway to verify the legality of the user; sending the device identity and the first timestamp of the device to a gateway to verify the validity of the device; when the user is legal and the equipment is effective, determining a group key, a user second timestamp, an equipment second timestamp, target equipment information and target user information based on the gateway; verifying the device according to the target device information and the second timestamp of the device; verifying the user according to the target user information and the second timestamp of the user; and when the user is legal and the equipment is valid, the group key and the equipment private key are sent to the equipment and the user, so that the security of data transmission in the Internet of Things is effectively guaranteed.

The invention belongs to the technical field of information security, and particularly relates to a private information retrieval-based blockchain intelligent parking management method, which comprises the following three stages: a blockchain network construction stage, including construction of a blockchain network structure based on an alliance chain and network initialization; the parking space information retrieval stage comprises the steps that a private information retrieval protocol is constructed, and a parking user retrieves interested parking space information on a block chain node; and the parking processing stage comprises the processes of reservation of a target parking space based on a retrieval result, entrance parking verification, parking, payment leaving, parking space state recovery and the like, and a transaction completion message is linked. Compared with an existing method, the method has the advantages that data storage is more complete and reliable, and the parking user query information privacy protection effect is good.

The present invention discloses an audio encryption and decryption method, comprising the following steps: obtaining audio data, dividing the audio data into a vocal part and a background sound part; processing the vocal part, encrypting the processing parameters, and obtaining a key M1; encrypting the vocal part, and generating a key M2 according to the audio attribute of the vocal part; encrypting the background sound, and generating a key M3 according to the audio attribute of the background sound; decrypting the processing parameters, the vocal part, and the background sound according to the key, restoring the processing of the vocal part according to the processing parameters, and synthesizing the restored vocal part with the background sound to obtain complete audio data. The present invention improves the difficulty of decryption by disassembling the audio data and encrypting it using the characteristics of different parts of the audio data, and solves the problem that the encryption degree of the prior art is low and the privacy security of users cannot be effectively protected.

The invention discloses a student cognitive diagnosis method based on data forgetting, and the method comprises the steps: obtaining the cognitive state information of a student through model pre-training, carrying out the data partitioning of the student according to the similarity, and independently carrying out the model training on each partition. And during student score prediction, calculating the similarity between the student and other students in each partition, further adjusting the prediction weight of each partition model, and finally aggregating and outputting a score prediction result. According to the forgetting framework, after the sensitive data is deleted, the partition data can be quickly retrained, and the training time is remarkably shortened. According to the method, the forgetting framework is constructed according to the cognitive state of the students on the knowledge points, so that the model is prevented from being interfered by dirty data and invalid data, and cognitive diagnosis is completed more accurately. According to the invention, the current cognitive state of the student can be diagnosed more accurately by clearing the outdated data in time.

The present invention provides a method for constructing a federated evolution multi-agent assisted feature selection model, which belongs to the field of data processing technology, including: collecting data sets; for any participant, performing filtering feature selection using an XGBoost tree model; multiple participants perform joint filtering feature selection by uploading feature position indexes with an importance of 0 to a trusted third-party server; all participants obtain a preliminary reduced feature subset RB; for any participant, initialize the initial model of the regression agent, initialize PSO algorithm related parameters; for any participant, perform a PSO-based agent-assisted federated evolution feature selection method on the obtained feature subset RB; and determine whether the current population of all participants meets the termination condition of the algorithm. The present invention can effectively solve the problem of high evaluation cost when the prior art processes multi-party privacy protection scenarios for joint modeling of high-dimensional data.

The present invention discloses a privacy-preserving clustering method and system. A querying party obtains data to be queried, performs homomorphic encryption on the data to be queried to obtain the encrypted data to be queried, obtains a training set held by a first central server agent, initializes an estimated vector of the data to be queried, and sends it to the first central server; each data holder learns the encrypted data to be queried based on a weak model trained with local data, obtains a weak learning result corresponding to the data to be queried under ciphertext, and sends it to the first central server; the first central server initializes a weight matrix, and based on the weight matrix and the weak learning result corresponding to the data to be queried under ciphertext, jointly interacts with a second central server through a dual-center agent to obtain an initialized aggregation result, and iteratively updates the aggregation result in combination with the estimated vector of each data to be queried, and sends the updated aggregation result to the querying party; after the querying party decrypts the aggregation result, it calculates and obtains a clustering query result, and the data to be queried by the customer will not be leaked.

The present invention proposes an information management method and system for access card chips, the method comprising: providing an anonymous identifier, initializing the authentication process, laying the foundation for privacy protection and data chain; completing mutual trust authentication between the access card and the device, and providing authentication challenge data for subsequent behavior recording and tracing; based on the results and challenge data generated after the authentication is completed, generating a complete encrypted behavior record and establishing a chain hash structure; storing the encrypted behavior record and the chain hash value locally or synchronizing them to a server to ensure the reliability and traceability of the behavior record; using the complete behavior chain data, detecting abnormal behavior and supporting traceability verification. The present invention significantly improves the privacy protection capability, security and tracing efficiency of the access control system by integrating dynamic identity generation, encrypted behavior chain storage and a two-way authentication mechanism in the access card chip.

The present invention provides a privacy protection method and device for audio information obfuscated reversible adversarial samples. The method proposes a new framework called "information obfuscated reversible adversarial examples". The framework can ensure the restoration of the original quality of the audio with authorization while ensuring the privacy security of the audio. By adopting the method of the present invention, the security and privacy protection level of audio data can be greatly improved, and unauthorized access and analysis can be effectively prevented. It is intended to solve the problems in the prior art of audio data privacy protection that adversarial attacks are easy to detect, the quality of audio is significantly reduced after processing, and the limitations of the existing reversible adversarial example (RAE) technology in audio application scenarios.

The invention discloses a privacy-enhanced access control method for medical data sharing, which is characterized in that an efficient and privacy-protected attribute revocation mechanism is innovatively added on the basis of an original decentralized multi-authority attribute verification and strategy hiding mechanism, and an attribute revocation list (ARL) based on a Merkle tree and corresponding ZKP constraints are introduced, so that the privacy-protected access control method for medical data sharing is realized. Fine-grained dynamic authorization is supported, the security problem that user permission changes along with time in a medical environment is effectively solved, and triple privacy security of user attributes, access strategies and attribute validity states is ensured. Through cooperative work of a system initialization stage, a strategy deployment stage, an access request submission stage, an access control decision stage and a data access stage, strong privacy protection, decentralized architecture, fine-grained control and dynamic authorization in medical data sharing are realized; the problems of single-point failure, privacy disclosure and strategy stiffness in a traditional scheme are effectively solved, and a safe and reliable technical basis is provided for cross-institution medical cooperation.

The application discloses a cloud-edge collaborative and federated learning-based electroencephalogram prediction model self-evolution system and method. The system comprises an edge device and a cloud server cluster: the edge device collects physiological signals of a user and runs a first machine learning model, and uploads desensitized data of a marked event asynchronously; the cloud server performs continuous wavelet transform on received one-dimensional electroencephalogram signals to generate two-dimensional time-frequency images, trains a second deep learning model by using the images, generates updated weights, and completes silent updating by issuing the updated weights to the edge device. The application solves the model concept drift problem through a cloud-edge collaborative architecture, realizes continuous evolution of a prediction model, improves feature extraction depth and generalization ability through time-frequency conversion and a multi-specialist sub-network architecture, and effectively protects user privacy through a data desensitization and asynchronous uploading mechanism.

The invention relates to a perception result determination method and device, communication equipment, a storage medium and a computer program product. The method comprises the following steps: receiving a perception service request, wherein the perception service request carries a perception service type, a perception service requirement and a target type of a perception result; generating a sensing data acquisition request and a sensing model acquisition request based on the sensing service type, the sensing service requirement and the target type of the sensing result; and calculating the sensing data based on the partial model of the target sensing model to obtain a sensing intermediate result, and sending the sensing intermediate result. By adopting the method, on the basis of ensuring the security privacy of the perception data, the calculation of the perception intermediate result can be realized through the partial model in the split perception model, and the perception data does not need to be opened to the third-party application, so that the third-party application can continue to process the perception service based on the received perception intermediate result; and normal operation of the sensing service is ensured.

This invention discloses a multi-agent air-to-ground network resource allocation method based on federated learning. In the air-to-ground network, the ground network constitutes high-data-rate V2I links; the air network constitutes V2U links for direct communication with ground vehicles; the V2U links share the spectrum resources of the V2I links and use hybrid spectrum access technology for transmission; a network resource allocation system model consisting of M pairs of V2I links and K pairs of V2U links is constructed; a multi-agent resource allocation method is adopted, and a deep reinforcement learning model is constructed with the goal of minimizing the total transmission delay of the V2I link channels; federated learning is used to optimize the deep reinforcement learning model; during the execution phase, the V2U links obtain their current state based on observations, and the optimal resource allocation strategy is obtained using the trained model. This invention exhibits good stability in highly dynamic air-to-ground networks.

The present disclosure relates to a page display method, a resource sending method, an electronic device, a server and a medium. The page display method includes: in response to detecting a trigger operation on a page jump link in a target application, obtaining a link request parameter required for the page jump link; encapsulating the link request parameter in the header information of the page jump link; sending a page request to a proxy server, the page request carrying the encapsulated page jump link, the page request being used to make the proxy server request the target server to feedback the page resource corresponding to the encapsulated page jump link; receiving the page resource fed back by the target server; and displaying the target page corresponding to the page resource in the target application. According to the embodiments of the present disclosure, it is possible to effectively prevent the user's privacy from being leaked, ensure the user's privacy security, and thus improve the user experience.

The present application provides a data privacy rating method, device, equipment, medium and product. The method includes: obtaining ciphertext user data; inputting the ciphertext user data into a privacy rating model, in which the forward reasoning process of the first M layers of the privacy rating model is calculated to obtain the ciphertext intermediate data of the first participant, the privacy rating model is a model trained based on a large language model, and M is a positive integer; when the ciphertext intermediate data of multiple second participants are obtained, the plaintext intermediate data is reconstructed based on the ciphertext intermediate data of the multiple second participants and the ciphertext intermediate data of the first participant; the plaintext intermediate data is input into the privacy rating model, and the remaining layers of the privacy rating model are calculated in plaintext to obtain the privacy rating result of the user data. The technical problem that the high robustness and low computational transmission overhead of data privacy rating cannot be simultaneously met in the related technology is solved.

The invention provides a multi-microgrid group optimization scheduling method, device and system based on cloud edge cooperation. A cloud server receives local scheduling parameters obtained and sent by each edge server through a microgrid scheduling model; the cloud server obtains an initial global scheduling parameter based on each local scheduling parameter and the power distribution network scheduling model; and for each edge server, executing an interactive iteration process based on the initial global scheduling parameter and a distributed optimization algorithm until an optimal global scheduling parameter is obtained. In the scheme, the data transmission quantity is effectively reduced and the overall scheduling efficiency of the system is improved through a cloud-edge collaborative hierarchical structure, and only the scheduling parameters are transmitted between the cloud layer and the edge layer, and sensitive information of the micro-grid is not acquired by the cloud layer, so that data isolation and privacy security among subjects are ensured, and the security of the micro-grid is improved. And in addition, a global optimal scheduling result is obtained by performing multiple interactive iterations by using a distributed optimization algorithm, so that the overall economy and the operation efficiency of the system are ensured.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a product service strategy analysis method, device, equipment and medium, and the method comprises the steps: recognizing a business stage corresponding to a target user according to an interaction record, and determining a service stage threshold value of the target user; identifying an interaction intention of the target user according to the multi-modal interaction features; identifying context data corresponding to the multi-modal interaction features, and analyzing an intention perception weight of the target user according to the interaction intention and the context data; performing parameter optimization on the local model, and adjusting service strategy parameters in the optimized local model according to the intention perception weight; and analyzing a product promotion index of the target user by using a local model corresponding to the adjusted service strategy parameter, generating a product decision instruction according to the service stage threshold and the product promotion index, and determining a service strategy of the target product according to the product decision instruction. And the accuracy of product service strategy analysis is improved.

The invention relates to a graph data processing technology, and discloses a federated graph data enhancement method based on graph limit estimation of privacy protection, which comprises the following steps that: a client obtains a social network graph, forms an original graph data set to generate graph limit estimation corresponding to a local tag, and performs differential privacy on the graph limit estimation; the server side calculates a pairwise cutting distance of each graph limit estimation, generates a plurality of nearest neighbors of the graph limit estimation, calculates a self-adaptive threshold value of each nearest neighbor, executes interpolation mixing according to the self-adaptive threshold values and updates the graph limit estimation; and the client receives the updated graph limit estimation to generate a to-be-optimized graph data set, and performs graph sampling on the to-be-optimized graph data set according to the quantitative proportion of the original graph data set to obtain graph enhanced data. The invention further provides a system of the federated graph data enhancement method for graph limit estimation based on privacy protection, the problem that topological structure characteristics of original graph data cannot be accurately reflected by existing graph data enhancement can be solved, and the security of privacy protection is improved.