76 results about "Privacy Level" patented technology

The invention discloses an urban multi-mode digital twin data asset security management method, and relates to the technical field of urban digital twin and data security, and the method comprises the steps: firstly building a unified access gateway, converging multi-department and multi-type data, and generating sensitivity annotation and privacy level metadata; performing differential privacy or desensitization and dynamically calibrating parameters at the local part of the data provider according to the labels; bookkeeping access, authorization and training on a permission alliance chain, and performing fine-grained permission control; through on-chain authorization, each node only uploads disturbed and encrypted model update, and a global model is obtained after secure aggregation; the governance layer continuously monitors logs, budget and model indexes and triggers strategy closed-loop regulation and control; and multi-agent reinforcement learning of a privacy agent and a performance agent is further introduced, a unified multi-target optimizer is formed through distillation, privacy, federation and permission strategies are refreshed online, and collaborative analysis and compliance sharing operation without data out of a domain are realized.

The invention discloses a financial data processing method and system based on machine learning, and a storage medium. The method comprises the following steps: encrypting customer credit data of a financial institution through Paillier homomorphic encryption and extracting a risk feature vector; performing secret sharing segmentation on the local gradient parameters and then safely aggregating the local gradient parameters into global gradient parameters through a BGW protocol; on the basis of the privacy level, global gradient parameter differential privacy noise is injected and subjected to federal training to obtain a cross-institution credit evaluation result; calculating risk characteristics and evaluation results through homomorphic encryption to obtain an encrypted credit score, and performing secure multi-party calculation and decryption to generate a customer credit report; and block chain supervision compliance verification is carried out to generate an audit record, and a privacy budget optimization scheme is dynamically adjusted according to the business emergency degree. The technical problems that in an existing financial data processing method, information is incomplete due to data islands, data leakage risks are caused by insufficient privacy protection mechanisms, and an effective supervision compliance verification mechanism is lacked are solved.

A social media platform facilitates user interaction through digital environments called “bubbles,” which serve as customizable, collaborative spaces for content sharing and engagement. Users can participate in gamified events such as challenges, races, and raffles within these bubbles. The platform incorporates a blockchain-based reward system that issues digital assets, including tokens and non-fungible tokens (NFTs), based on user participation and performance. Each piece of content shared within a bubble can be assigned its own privacy level, independent of the user's account settings. The system supports role-based access, content moderation, and smart contract integration to ensure secure, transparent reward distribution.

The invention discloses a method and system for privacy data protection in a big data analysis process, and relates to the technical field of civil aviation data security and privacy protection, and the method comprises the steps: obtaining civil aviation multi-source heterogeneous data, extracting passenger identity features, travel track features and consumption behavior features, and generating a structured feature matrix; extracting an association relationship between fields through a convolutional neural network, calculating a field sensitivity initial value, generating a field sensitivity weight in combination with a field uniqueness index and the sensitivity initial value, and generating a privacy level mapping table containing the field sensitivity weight according to the sensitivity weight; calculating query sensitivity and a privacy budget parameter, generating a differential privacy noise parameter and a homomorphic encryption key, and generating a hierarchical protection strategy; performing noise injection and ciphertext conversion on the original data to generate a desensitized data set; passenger flow statistics is executed through secret state calculation, and a public analysis result is generated. Over protection of low-sensitivity data is avoided, and sufficient safety of high-sensitivity data is ensured.

Aiming at privacy protection mean value estimation of multi-dimensional numeric data, a personalized privacy protection mechanism meeting-localization differential privacy is designed, and personalized privacy protection of a user level and a data level is provided. According to the invention, each user can select one privacy protection level from a plurality of preset privacy protection levels according to own privacy protection requirements, so that personalized privacy protection of the user level is realized, which is the first personalized privacy protection. And based on the expectation of the minimum estimation variance, determining an optimal dimension extraction parameter, and randomly extracting part of dimension data from all dimensions to carry out disturbance submission. And a user scoring strategy is adopted to determine a distribution strategy of privacy protection parameters, so that personalized privacy protection on a user data level is realized, which is second personalized privacy protection. And finally, a weighting factor is constructed based on the expectation of the estimation variance, and a weighted combination mode is adopted for mean value estimation under multiple privacy levels, so that the accuracy of the overall mean value estimation is further improved.

The invention provides a privacy computing service method and system based on federal learning. According to the method, desensitized data is generated by identifying sensitive field types of original data of federated learning participants, dividing privacy levels and carrying out local shielding and superposition of dynamic noise. Dividing the data into data blocks in direct proportion to the number of the arithmetic units based on the complexity of an encryption algorithm, performing parallel encryption, transmitting the data blocks to the cooperative node through an encryption channel to obtain an aggregation result, and synchronously recording time consumption and a privacy intensity value. The data block segmentation number is dynamically adjusted by calculating the time consumption difference value, and correlation parameters are generated through cross validation with the privacy intensity value. When the privacy intensity is insufficient, the dynamic noise of the corresponding level is improved, data block segmentation is synchronously optimized, and a dynamic linkage mechanism of noise enhancement and block encryption is formed. Through a dynamic mutual feedback mechanism of the difference value and the privacy intensity, real-time balance between the data desensitization intensity and the encryption calculation efficiency is realized.

The invention relates to the technical field of image processing, in particular to an image data desensitization method, device and equipment, and the method comprises the steps: extracting a to-be-processed image or a RAW domain image of a to-be-processed video stream; sequentially performing wide dynamic range processing, first Bayer noise processing and tone mapping processing on the RAW domain image to obtain a first processed image; obtaining a dynamic ROI detection model and a privacy grading model; based on the first processing image and the dynamic ROI detection model, a second processing image is output, and the second processing image is that a privacy area is marked on the first processing image; based on the second processing image and a privacy grading model, obtaining a privacy level comprehensive score of each privacy area; and determining a corresponding desensitization means based on the privacy level comprehensive score of each privacy area so as to carry out pixel level privacy processing on the second processing image, and realizing end-to-end security privacy protection by processing the RAW domain image of the video or the image.

Methods of enabling Artificial Intelligence (Al) models to be trained in a privacy preserving manner are disclosed. A method comprises the formation of virtual cluster per privacy level and selection of a cluster head that performs the training and coordination per privacy level. A method comprises training an Al primary model on a private user data set. The Al primary model may be used to label a public data set. A secondary model may be trained using the labeled public data set. The secondary model may be shared with a network or other UEs.

The invention discloses an Internet of Things video monitoring big data privacy protection and efficient retrieval method based on artificial intelligence, belongs to the crossing field of artificial intelligence, Internet of Things and information security, and is suitable for vehicle-mounted, park, battery swap stations and other scenes. The method comprises the following steps: firstly, establishing a self-adaptive acquisition framework, and realizing multi-protocol switching and video preprocessing; secondly, extracting privacy information through an improved YOLO algorithm and a Graph-Cut technology, and combining reversible watermark embedding; constructing a multi-dimensional privacy level model for hierarchical encryption, and matching two-level storage and three-level index; then, two-factor authentication authorization is performed to extract privacy and optimize retrieval; and finally, the system state is monitored in real time and self-adaptive optimization is performed. According to the method, the problems of protocol heterogeneity, insufficient privacy protection, low retrieval efficiency and the like can be solved, and privacy security, storage overhead and retrieval efficiency are balanced.

The invention discloses an efficient graph abstract system and method integrating privacy weighted neighborhood similarity and a graph neural network. The method comprises the following steps: carrying out privacy grading labeling on original graph data; performing preliminary grouping on the nodes based on privacy weighted neighborhood similarity measurement; based on a graph neural network, constructing a module integrating a graph attention mechanism and reinforcement learning, and performing super-node selection and combination; a merging constraint rule based on the privacy level is set, and an abstract graph with the controllable privacy level is output; the method can realize balance and optimization of abstract efficiency and privacy protection capability, and can be widely applied to scenes with strict requirements on graph storage and privacy.

One embodiment of the present invention sets forth a technique for providing awareness of privacy-related activities. The technique includes determining a privacy level associated with a user of an extended reality environment. The technique also includes presenting, using an internal display of a headset, one or more internal indicators identifying a location of a bystander located in a real-world environment, wherein a level of detail of each internal indicator is based on the privacy level associated with the user. The technique further includes presenting, using an external display, one or more external indicators that include a monitoring indicator representing being captured by the headset and presented to the user via the headset, and further include a user activity indicator representing one or more activities of the user, wherein a level of detail of the user activity indicator is based on the privacy level associated with the user.

The invention discloses an Internet hospital supervision data uploading method and system, and relates to the technical field of data supervision, and the method comprises the steps: receiving multi-source heterogeneous supervision data and real-time network state parameters, recognizing a compliance attribute tag and a privacy security level tag of a data unit according to a preset supervision knowledge graph, and outputting a supervision data stream; the supervision data stream is input into a game theory analysis engine, an optimal cooperation strategy combination is analyzed through a Nash equilibrium algorithm, and a bandwidth allocation weight, a transmission priority queue and a dynamic compression threshold are output; injecting the verified data block and the metadata verification anchor point of the verified data block into the block chain fragment to perform smart contract consistency verification, and outputting an auditable data unit; and the auditable data integration unit generates a supervision evidence storage voucher and synchronizes the cloud and the edge to complete data uploading. According to the method, adaptive balance of the compression ratio and the data integrity is realized in a bandwidth dynamic fluctuation environment by adopting a neural differential compression operator and a manifold feature extraction mechanism.

The invention relates to a method for realizing multi-modal large-model fine-grained privacy grading protection in an edge-cloud collaborative inference system, belongs to the technical field of large-model data security and privacy protection, and aims to solve the problems that privacy grading is rough, inference intermediate features are easy to leak and precision is damaged by protection measures in the prior art. According to the method, multi-modal data such as images and texts are received by edge computing nodes, deep semantic analysis and fine-grained segmentation are performed by using a local pre-trained multi-modal large model, and privacy scores are calculated to divide high and low privacy levels; uploading low-privacy data to a cloud computing center, and locally processing high-privacy data; and selectively shielding a middle layer feature map F generated by the edge based on the correlation degree and the contribution degree, then completing local reasoning, and uploading a result to be fused with the cloud. According to the method, precise fine-grained protection is achieved, the sensitive information leakage risk is remarkably reduced, and meanwhile the model reasoning precision and the system real-time performance are kept to the maximum degree.

The invention relates to the technical field of Internet of Things data sharing, and discloses an Internet of Things data intelligent sharing method and system, and the method comprises the steps: obtaining perception data in a home network through an edge device, and carrying out the privacy data and non-privacy data classification of the data; calculating a privacy level by adopting an information entropy quantification method, and selecting a proper transmission protocol according to network state evaluation and equipment energy consumption; if the performance does not reach the standard, automatically switching to a low-power-consumption protocol; and when the transmission performance is good, multi-protocol parallel transmission is started. Compared with the prior art that a traditional data sharing method usually depends on a static protocol selection and encryption mechanism, and especially in a home network scene, the technical problem that balance among equipment energy consumption, network quality change and privacy protection requirements is ignored is solved. Due to the fact that a dynamic protocol selection mechanism is introduced, balance between privacy protection and data transmission efficiency is achieved, and the efficiency, safety and adaptability of data sharing of the Internet of Things are improved.

The embodiment of the invention discloses a safety calculation method and device for intelligent workshop device coordination and a medium, and relates to the technical field of safety calculation, and the method comprises the steps: carrying out the on-chain device authentication of a plurality of device terminals in a target intelligent workshop in advance, so as to obtain the device production data corresponding to the target device terminal passing the authentication, performing sensitivity grading on the equipment production data, and determining a corresponding dynamic privacy level; matching a corresponding data encryption strategy according to the dynamic privacy level, determining a plurality of data encryption fragments corresponding to the equipment production data based on the data encryption strategy, and storing the data encryption fragments to a preset distributed node; and obtaining a current collaborative scheduling task of the target intelligent workshop, performing security calculation on the predetermined associated data encryption fragment through a predetermined edge calculation node based on a mixed framework of a confusion circuit and arithmetic secret sharing, generating a collaborative scheduling instruction, and feeding back the collaborative scheduling instruction to the target equipment terminal, therefore, safe calculation of equipment cooperative scheduling is realized.

A method, computer system, and computer program product are provided for assessing workspaces for privacy and allocating users to workspaces. Audio is obtained from one or more rooms of a building, wherein the audio is obtained via one or more microphones that are positioned outside of each of the one or more rooms. The audio is analyzed to determine a privacy level for each of the one or more rooms. A user is identified who is present in the building, wherein an identity of the user indicates privacy requirements of the user. The user is assigned to a particular room of the one or more rooms based on the privacy requirements of the user and the privacy level of each of the one or more rooms.

Embodiments described herein relate estimating a privacy level of an anonymized dataset based on an anonymization strategy selected using parameters of the dataset that form a dataset profile. Methods may include: receiving a dataset defining at least one trajectory; determining parameters of the dataset; generating a profile of the dataset based on the parameters of the dataset to establish a dataset profile; identifying a predetermined profile corresponding to the dataset profile; determining an anonymization strategy corresponding to the predetermined profile; anonymizing the dataset using the anonymization strategy to generate an anonymized dataset; and publishing the anonymized dataset for use with location-based services. According to some embodiments, identifying the predetermined profile corresponding to the dataset profile includes identifying a predetermined profile having parameters within a predefined degree of similarity of the parameters of the dataset.

The present application discloses a service management and reasoning service method, device, storage medium, and program product, which relate to the field of cloud service technology. The service management method includes pre-configuring the privacy information of the target service in the configuration request, and can dynamically generate a configuration requirement file adapted to the target service based on the privacy information before creating the target service. In this way, when creating the target service, the corresponding service environment can be built according to the configuration requirement file to adapt to the privacy level required by the target service. Therefore, the technical problem of unreliable service management is solved, and the technical effect of adapting the dynamic management of each service to improve the security of service management and the reliability of service operation is achieved.

The present invention concerns a method for managing a privacy level in a wireless network comprising an access point, AP, station and at least one non-AP station, the method comprising by a first station, either a non-AP station or the AP station sending, to a second station a message comprising an information indicating a set of privacy enhancement, PE, parameters to be obfuscated, the set of PE parameters corresponding to a privacy level requested by the first station, the second station being the AP if the first station is a non-AP station, the second station being a non-AP station if the first station is the AP, and receiving from the second station a response indicating whether the second station accepts the requested privacy level.

The invention discloses a hierarchical privacy protection data sharing method and device based on classification transformation perturbation, and the method comprises the steps: a terminal user freely selects a privacy budget, carries out the standardized preprocessing of numeric privacy data, carries out the classification of the preprocessed privacy data, and carries out the classification perturbation under the corresponding privacy budget. And carrying out classified restoration operation to output privacy-protected data, and uploading the classified and restored data and the corresponding privacy level to a server for hierarchical storage. And finally, grading data sharing is performed on the data under the multiple privacy grades, and the data users of the corresponding credibility grades can only obtain the disturbance reduction data under the corresponding privacy budget for data analysis. According to the method, a new solution is provided for high-availability hierarchical privacy protection mean value estimation.

An image grading privacy protection method and device and a storage medium are applied to the technical field of image encryption sharing, and the method applied to a first device comprises the following steps: sequentially encrypting a target image according to image encryption keys respectively corresponding to a plurality of privacy grades to obtain an encrypted image; acquiring encrypted public and private keys generated based on LWE and corresponding to each user level; according to a preset encryption algorithm and an encryption public key corresponding to the first target user level, encrypting an encryption private key of a second target user level to obtain a public value, the second target user level being any user level except the user level with the highest priority, and the first target user level being one priority higher than the second target user level; according to a preset encryption algorithm and the encryption public key corresponding to each user level, encrypting the corresponding image encryption key to obtain a plurality of ciphertexts; and sending the encrypted image and all ciphertexts and public values to a second device. And the data security and quantum security of image data and key transmission are improved.

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 invention provides a data delivery method, device, medium and system, and the method comprises the steps: determining a target privacy calculation protocol according to a data type and a privacy level before data delivery, or determining the target privacy calculation protocol according to the data type; binding a data use constraint, a privacy calculation parameter and a participant digital identity by adopting an intelligent contract; and according to the target privacy calculation protocol and the bound smart contract, constructing a data delivery application program, and executing the data delivery application program to complete data delivery, the data delivery application program being an application program matched with the data product type. The problem that in the prior art, a unified application program development standard is not formed, so that data delivery tools are fragmented, and compatibility is poor is solved.

A method, computer system, and computer program product are provided for assessing workspaces for privacy and allocating users to workspaces. Audio is obtained from one or more rooms of a building, wherein the audio is obtained via one or more microphones that are positioned outside of each of the one or more rooms. The audio is analyzed to determine a privacy level for each of the one or more rooms. A user is identified who is present in the building, wherein an identity of the user indicates privacy requirements of the user. The user is assigned to a particular room of the one or more rooms based on the privacy requirements of the user and the privacy level of each of the one or more rooms.

An advertisement recommendation method and an electronic device, relating to the technical field of terminals. The electronic device can choose to send partial intent data with a low privacy level to a server, so as to implement advertisement recommendation, thereby meeting the requirements of a user for privacy protection during an advertisement recommendation process. The method comprises: an electronic device sending first intent data to a server; the server acquiring a first advertisement list on the basis of the first intent data, wherein first content in the first advertisement list is related to the first intent data; the server sending the first advertisement list to the electronic device; and after receiving the first advertisement list, the electronic device adjusting the ranking of the first content in the first advertisement list on the basis of second intent data, and acquiring a second advertisement list, wherein a first privacy level of the first intent data is lower than a second privacy level of the second intent data, and the second advertisement list is used for displaying advertisements.

The invention provides an advertisement recommendation method and electronic equipment, and relates to the technical field of terminals. According to the application, the electronic equipment can selectively send part of intention data with low privacy level to the server to realize advertisement recommendation, so that the requirement of a user for privacy protection in an advertisement recommendation process is met. The method comprises the following steps: the electronic equipment sends first intention data to a server; thereafter, the electronic device may obtain a first advertisement list, the first content in the first advertisement list being related to the first intent data. If yes, the electronic equipment adjusts the sequence of the first content in the first advertisement list according to the second intention data to obtain a second advertisement list. Wherein the first privacy level of the first intention data is lower than the second privacy level of the second intention data, and the second advertisement list is used for displaying advertisements.

The application discloses a face image identity privacy protection method and device, the method comprises the following steps: constructing a feature learning model, extracting features of an acquired face image by the feature learning model, and decoupling statistical information and background information in the face image by a face identity feature decoupling subnetwork model; integrating the features extracted by the feature learning model and the statistical information to obtain integrated information; constructing an identity disturbance model, adding random disturbance to the integrated information based on a privacy level of a face image user; and constructing an image reconstruction model, reconstructing the face image based on the integrated information after adding random disturbance. The method of the application can effectively resist background knowledge attacks, reconstruction attacks and combination attacks, realize provable and controllable face identity privacy protection, and ensure the optimization trade-off between the privacy and usability of the published image.

The present disclosure relates to a method and an apparatus for performing communication in a wireless communication system performed by a first electronic device. The method for performing communication may comprise: a step of receiving a private call request message containing information on a first privacy level from a second electronic device; a step of identifying whether a privacy setting corresponding to the first privacy level is supported; and a step of performing a call with a second electronic device based on the privacy configuration when the privacy setting is supported.

The invention discloses a privacy protection method and system based on Internet of Things data encryption transmission, and the method comprises the following steps: a data collection step: collecting original data from at least one Internet of Things device, the Internet of Things device including but not limited to a sensor, an actuator, a smart home device or an industrial Internet of Things node, the original data comprises one or more of environment data, user behavior data and equipment state data, the acquisition frequency is dynamically adjusted according to equipment types and application scenes, the range is one time per second to one time per hour, and a tamper-proof mechanism is adopted in the acquisition process to ensure the data integrity; the core advantage of the method is that refined and quantitative evaluation of the data privacy level is realized by introducing an innovative privacy score calculation formula.