106 results about "Privacy Level" patented technology

The invention discloses a trusted cloud security confidential privacy level product service method, and relates to the technical field of design of cloud computing security. The method comprises the following steps: acquiring a trusted cloud privacy security level and a hierarchical core security mechanism, establishing a binding relationship with a product, extracting technical features and performing quantitative scoring, and constructing a three-dimensional security matrix; obtaining a privacy grading service demand of the user, and establishing a demand matching model to screen a security preference grade product; obtaining different-dimension technical grade improvement times of the user security preference grade product, and carrying out synergistic effect analysis; if significant positive correlation exists, correlation analysis is carried out to obtain a dynamic correlation coefficient, a product optimization strategy is made, a three-dimensional security matrix is dynamically updated, and the trusted cloud security confidential privacy level product service system comprises a feature classification module, a product optimization module, a collaborative analysis module and an optimization updating module. According to the method, more accurate security product recommendation can be provided for the user, and the trusted cloud security service level is improved.

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.

The invention discloses a multi-technology fusion multi-source data grading and classifying method and system, and the method comprises the following steps: S1, determining a data source, collecting multi-source data, and carrying out the preprocessing; s2, monitoring by using a CDC technology, and extracting monitored target data; s3, performing feature extraction on the target data, and introducing an EMAP method to perform nonlinear cross projection on a multi-modal feature set; s4, inputting the high-dimensional feature vector into a hybrid classification network, and executing main component molecular space compression and unsupervised clustering; s5, mining high-frequency attribute items by adopting an FP-Growth algorithm, and constructing a privacy attribute set and a non-privacy attribute set; s6, calculating the weighted privacy degree of each privacy cluster, and performing multi-layer privacy level division through a hierarchical mapping network; and S7, carrying out encryption and desensitization processing on the data under each type of labels, and carrying out visual output. According to the invention, the intelligence and security of multi-source data classification and privacy grading are improved.

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 user privacy protection method and device for a face recognition system, and belongs to the technical field of mobile equipment and software security, and the method comprises the steps: carrying out the edge detection and lightweight semantic segmentation parallel processing of an input image, and generating edge strength mapping and semantic confidence mapping; generating a privacy grading mask through dynamic weight fusion edge strength mapping and semantic confidence mapping; dividing the image into three levels of privacy areas through a privacy grading mask; performing hierarchical encryption on the three-level privacy area through three non-periodic chaos sequences generated in advance to complete user privacy protection; wherein the three non-periodic chaotic sequences are iteratively generated through a Young's hyperchaotic system based on a Hash value spliced by a user-defined key and system time; according to the invention, picture encryption according to the privacy level is realized, and the user privacy in face recognition is protected.

The invention is suitable for the technical field of privacy data processing, and provides a privacy grading and sharing control method for tourist travel itineraries, and the method comprises the steps: obtaining travel itinerary data of tourists; dividing the travel data into at least three basic privacy levels based on a preset region sensitivity classification rule, a user identity classification rule and an equipment type classification rule; in the basic privacy level, performing fine-grained sub-level division on the data according to interval division of travel duration, classification of activity risk levels and a historical sharing frequency threshold; based on the basic privacy level and the fine-grained sub-level, geographic position fuzzification and user identity anonymization processing are carried out on the travel data of the high-sensitivity level, and the access range of a shared object is limited through permission grading; and the sensitivity of the fine-grained sub-level is adjusted according to the real-time sharing frequency, and the sharing permission is updated, so that the accurate balance of the travel itinerary data between privacy protection and sharing effectiveness can be realized.

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 relates to the technical field of data security, in particular to a data security agent system of a trusted data space, which comprises the following steps: dividing the trusted data space into a plurality of data systems, calculating privacy scores of single data sets in each data system, performing location arrangement of privacy levels on the single data sets through the privacy scores, and performing location arrangement of the privacy levels on the single data sets through the privacy scores; the method comprises the following steps: obtaining a privacy level sequence, identifying basic identity information of a target visitor, determining a reference visitor, analyzing a historical access record of the reference visitor, obtaining an access keyword of the target visitor, calculating a data association value between a system keyword and the access keyword, comparing the data association value with the privacy level sequence, determining open information, and displaying the open information to the target visitor. In the data opening process, the privacy risk of the data is fully considered, and data leakage caused by excessive opening is avoided.

A video capturing system includes: a storage medium to store a plurality of image information sets corresponding to a plurality of reference images, respectively; and a video processing device to selectively control video capture and at least one other operation associated with video capture according to a privacy level selected from a privacy mode, wherein the video processing device includes a face detector to sense a face image from the captured video, and a privacy level controller to select one of a plurality of privacy levels according to an image information set matched with the face image sensed from the plurality of image information sets.

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.

The invention discloses an intelligent control method and system for dynamic shielding of a guest room, and belongs to the technical field of intelligent control. According to the method, distance data between personnel and equipment and environmental acoustic characteristic data are collected through a multi-modal sensor, a dynamic evaluation model is constructed, and the model comprises a linear fusion structure and an integrated architecture with graph structure modeling and uncertainty reasoning capabilities and is used for outputting a privacy risk evaluation value. And judging a privacy risk level according to the evaluation value, triggering a corresponding optical, acoustic or physical shielding response, and generating a shielding control instruction. The instruction is issued to compatible shielding execution equipment through a protocol adaptation mechanism to realize dynamic shielding control; and the system automatically enters a low-power-consumption mode when the low-risk condition is met. According to the invention, privacy level intelligent discrimination and multi-level shielding response control based on scene perception are realized, the adaptability, the control precision and the energy efficiency level of the system are improved, and the method is suitable for scenes with privacy requirements, such as intelligent guest rooms and conference rooms.

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 a dynamic fragmentation desensitization privacy protection method based on a block chain. The method comprises the following steps: data sensitivity analysis; performing dynamic fragmentation processing; multi-level privacy protection is realized; a double-chain storage architecture; efficient privacy verification; and the access control and auditing module is used for monitoring and detecting the behavior of abnormally accessing the high-sensitivity data. According to the method, a sensitivity-driven dynamic fragmentation desensitization algorithm is adopted, data security and availability are considered, and fragmentation modes including full desensitization, partial desensitization and format reservation encryption are adaptively selected according to data types, access frequency and privacy level factors; according to the method, improved Bulletprofs zero knowledge is adopted for proving, the verification calculation complexity is reduced by 50% by reducing the number of times of Pedersen commitment generation, parallel calculation is supported, and the throughput is remarkably improved.

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.