413 results about "Individual data" patented technology

The invention relates to the technical field of campus management, and particularly discloses a smart campus management system based on big data, an event-driven data management architecture is used for dynamically collecting, integrating and associating multi-source heterogeneous data in a campus, and generating a standardized event stream; the dynamic resource scheduling engine is in communication connection with the event-driven data governance architecture, generates a resource allocation instruction based on event types and priorities in event streams, and dynamically deploys campus resources; the closed-loop evaluation optimization module receives a resource scheduling result of the dynamic resource scheduling engine and generates a multi-dimensional evaluation index, and the multi-dimensional evaluation index is fed back to the data governance architecture through root cause analysis so as to optimize a subsequent decision; the privacy enhancement processing unit integrates a federated learning framework and a differential privacy algorithm, performs collaborative analysis on cross-system data and ensures the anonymity of individual data; through three core technologies of dynamic data management, intelligent resource scheduling and closed-loop evaluation optimization, intelligent upgrading of the whole campus management process is realized.

Various systems, computer-readable media, and computer-implemented methods of providing improved data privacy, anonymity and security by enabling subjects to which data pertains to remain “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent that is desired—are disclosed herein. Embodiments include systems that create, access, use, store and / or erase data with increased privacy, anonymity, and security—thereby facilitating the availability of more qualified and accurate information. When personal data is authorized by data subjects to be shared with third parties, embodiments described herein may facilitate the sharing of information in a dynamically-controlled manner that also enables the delivery of temporally-, geographically-, and / or purpose-limited information to the receiving party. In one example, the disclosed techniques may be used to functionally separate geospatial information, such that it remains “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent or degree that is desired.

The invention belongs to the technical field of cross-border data security, and provides a cross-border personal data transmission security detection method and system based on differential privacy, and the method comprises the steps of multi-dimensional situation awareness, privacy budget allocation, interpretability and audit, and execution and response. By synchronously sensing technical environment risks, multi-national regulation constraints and multi-modal data features, comprehensive control of the cross-border transmission security situation is realized, and one-sidedness of single-dimensional sensing is avoided; privacy budget is dynamically allocated under multiple constraints based on a deep Q network algorithm, so that the protection intensity of high-sensitivity data is ensured, and excessive loss of effectiveness of low-sensitivity data is avoided; an interpretable report is generated in combination with causal analysis, full-process evidence storage is realized by relying on an alliance chain, the problems of decision black box and audit tracing are solved, cross-border supervision requirements are met, and the balance of cross-border transmission in the aspects of safety, compliance and availability is improved.

The invention discloses a medical clinical data quality analysis method and system based on a knowledge graph, and relates to the technical field of medical data analysis. The method comprises the following steps: constructing a dynamic medical field knowledge graph fusing medical ontology and semantic relationship, accessing multi-source heterogeneous clinical data into the graph through entity linking and standardized mapping, and generating a personal data sub-graph containing time sequence information for each patient; through combination of logical reasoning based on a semantic rule base and anomaly detection based on a graph neural network, deep discovery and accurate positioning of known contradictions and unknown mode quality problems are realized, and a targeted repair scheme is generated by utilizing the traceability of a graph; quality problems are quantitatively graded and fed back to the atlas, and closed-loop management is formed; according to the method, the limitations of isolated examination and lack of semantic understanding of a traditional method are overcome, the transformation from passive verification to active prevention is realized, and the control precision and efficiency of the medical data quality are remarkably improved.

A computer-implemented apparatus, system, and method is disclosed for protecting sensitive data. A cognoscible computing engine is multi-layered and multi-pathed. It includes features for handling different data formats, including structured, semi-structured, and unstructured data. Features are included to support near real-time processing at scale with high accuracy. Applications include redacting or masking sensitive data to comply with data privacy and security standards.

Systems, methods, and other embodiments associated with quasi-supervised clustering for activity pattern characterization and anomalous activity detection are described. In one embodiment, a method generates a first sparse similarity matrix for nearest neighbors of a plurality of data points. The data points each characterize a pattern of activity associated with an account. The method generates a second sparse similarity matrix for random neighbors of the plurality of data points. The method recursively clusters the plurality of data points based on the first sparse similarity matrix. The method quasi-supervises the recursive clustering based on the second sparse similarity matrix to stop the iterative clustering when the data points are split into N clusters. The value of N is not pre-determined. The method detects that the individual data point has changed clusters, indicating anomalous activity. And, the method generates an electronic alert that the anomalous activity is associated with the account.

The invention provides an electric vehicle cluster parameter prediction method and device and computer equipment, and the method comprises the steps: carrying out the pre-training of a bidirectional long-short-term memory network according to commercial charging pile data, and enabling the bidirectional long-short-term memory network to be used for predicting the charging behavior parameters of an electric vehicle in combination with a historical charging track and a future charging trend; based on authorized household charging pile data, a transfer learning algorithm is adopted to carry out fine tuning on the pre-trained bidirectional long-short-term memory network, and a charging behavior prediction model is obtained; inputting the historical charging data of each electric vehicle into a charging behavior prediction model to obtain predicted charging data of each electric vehicle; and according to each piece of predicted charging data, constructing a monomer feasible region of each electric vehicle, and according to each monomer feasible region, obtaining a cluster feasible region of the electric vehicle cluster through sino polyhedron modeling and Minkowski summation. Therefore, the problems of limited individual data and long time span are relieved, and the accuracy and practicability of electric vehicle cluster parameter prediction are further improved.

The invention discloses a multi-layer heterogeneous sequential network feature alignment and clustering method and device based on tensor self-representation. The method comprises the steps that sequential feature matrix sequences {},..., {} of data views in a multi-view heterogeneous network in time windows are obtained; based on the matrix sequence of the first time window, constructing a Laplacian regular term by introducing a self-representation learning mechanism, modeling multiple views by adopting a tensor structure and introducing a Schatten p-norm regularization mode to construct an optimization objective function for optimization, and representing shared feature representation of each data view in the first time window; and solving the function and carrying out clustering processing on the obtained function, wherein the clustering result of each time window is used for carrying out characteristic analysis on nodes in the network. According to the method, on the premise that the original structure and semantics of the network are guaranteed, unified modeling can be carried out on the incomplete multi-view heterogeneous network, and feature expression is aligned.

The invention relates to the technical field of information processing, in particular to a medicine conflict automatic detection and prescription optimization system for old multi-disease patients. According to the system, a data management module is used for collecting individual data of patients and group data of co-diseased reference groups and receiving a planned medication scheme; the parameter calculation module is used for identifying a target medicine combination with time overlapping in the patient medication record and the planned medication scheme; determining a basic risk index according to the group medication response data, the patient medication record and the work and rest text data; determining a lag risk coefficient according to the physiological indexes and the drug metabolism data; determining group medication associated risk parameters according to atypical reactions and group medication records in the medication reaction data; the risk fusion module is used for determining a drug use conflict risk value based on the basic risk index, the lagging risk coefficient and the group drug use associated risk parameter; and the prescription optimization module is used for generating an optimal medication scheme, so that the generated optimal scheme is more suitable for the actual life of the patient.

The invention belongs to the technical field of computer server management, and particularly discloses a laboratory collaborative environment-oriented user and data full-life-cycle server management system, which comprises a container virtualization layer, a server management layer and a server management layer, dynamic resource allocation is supported through a state saving-resource reallocation-environment recovery mechanism; the heterogeneous transaction coordination layer adopts a Saga transaction mode to coordinate a heterogeneous system so as to realize cross-system transaction consistency; the data collaboration ecological layer establishes typed management of a personal data volume, a shared software environment volume and a public data set volume, and supports software environment encapsulation sharing and versioning management; and the metadata management layer realizes real-time update and high-performance query of metadata based on a Redis hot update architecture. According to the method, the problems of inflexible resource allocation, incomplete environment isolation, dilemma in authority management and difficulty in data collaboration and recovery in the prior art are solved.

The invention relates to the technical field of artificial intelligence and legal information processing, in particular to a security legal system intelligent question-answering system based on an RAG architecture, and is used for solving the problems of insufficient real-time performance, poor speciality of generated content, computing power dependence, data quality deviation, weak knowledge association and the like of a general large model in a legal scene in traditional legal consultation. According to the technical scheme, the method comprises the following steps: dynamically partitioning a law knowledge base, and constructing a high-dimensional vector index based on a BERT model to support semantic retrieval; calculating by fusing TF-IDF content similarity and cosine semantic similarity, and optimizing a retrieval result in combination with a sorting learning model; a rigorous Prompt template constraint generation logic is built in, legal provisions are cited forcibly, an efficacy level is marked, and an optimal result is screened through a multi-candidate answer evaluation mechanism. The system realizes efficient matching and specialized output of legal clauses, judicial cases and law knowledge, provides intelligent and highly credible legal consultation services for national security, personal data protection and common law scenes, and promotes deep fusion of artificial intelligence and legal information processing technologies.

An intelligent assistant incorporating a Large Language Model (LLM) can transform a transcript containing communications regarding tasks into structured data. The transcript can be a meeting transcript or a transcript of a chatbot chat that was autonomously initiated by the intelligent assistant to obtain additional information regarding a task. With LLM assistance, the intelligent assistant transforms unstructured communication data from the transcript into structured data which is then assigned to relevant task objects stored in a database. Prior to processing a transcript, personal data therein can be sanitized, and the intelligent assistant can divide the transcript into smaller segments which each encapsulate a discussion of a different topic.

System and method for managing interaction of data between Artificial Intelligent (AI) agents within a secure cloud-based enclave are disclosed. The method comprises initiating, by a single AI agent, an interaction request with a shared AI agent. The shared AI agent triggers a negotiation and authorization process to the single AI agent based on the interaction request. The negotiation and authorization process determines whether the single AI agent is eligible to interact with the shared AI agent. The shared AI agent receives user data from the single AI agent when the single AI agent is eligible to interact with the shared AI agent. The shared AI agent categorizes the user data into data sets based on a type of the user data. The shared AI agent generates personalized recommendations based on the data sets.

The invention provides a user data access method and device, equipment and a storage medium, and the method comprises the steps: analyzing an authorization code corresponding to a target user when a data access request is received, and obtaining the authorization information of the target user; matching data access meta-information corresponding to the data access request with the authorization information; and if the matching is successful, returning the target data associated with the data access meta-information to the data access request system. Through the implementation of the scheme, the authorization information can be obtained by analyzing the authorization code of the target user, whether the target data needed by the data access request is returned or not is judged according to the authorization information, the authorization code is designed and generated according to the personalized demand of the user, and therefore the user can have the control right to process the personal data of the user, and the user experience is improved. And the access request is ensured to accord with the self-defined authorization range, so that the user data security is effectively improved.

Systems, methods, and other embodiments associated with quasi-supervised clustering for activity pattern characterization and anomalous activity detection are described. In one embodiment, a method generates a first sparse similarity matrix for nearest neighbors of a plurality of data points. The data points each characterize a pattern of activity associated with an account. The method generates a second sparse similarity matrix for random neighbors of the plurality of data points. The method recursively clusters the plurality of data points based on the first sparse similarity matrix. The method quasi-supervises the recursive clustering based on the second sparse similarity matrix to stop the iterative clustering when the data points are split into N clusters. The value of N is not pre-determined. The method detects that the individual data point has changed clusters, indicating anomalous activity. And, the method generates an electronic alert that the anomalous activity is associated with the account.

An apparatus to facilitate capability-based memory access control for graphics processors and accelerators is disclosed. The apparatus includes one or more processing cores to: determine that a set of data accesses triggers a bulk access capability check; generate the bulk access capability check to combine with the set of data accesses; prior to performing a first data access of the set of data accesses, perform the bulk access capability check to check that the set of data accesses is allowed to be accessed in accordance with a capability; and responsive to the bulk access capability check passing, allow the set of data accesses to proceed without performing a capability check for each individual data access of the set of data accesses.

The invention relates to a system and method for operating fraud prevention. The present invention aims to resolve those issues encountered when individuals are targeted by a potential fraudster, such as (i) enhance the security of accessing individuals digital assets / accounts, (ii) allow the onboarding of passbook users or account holder identification item users with an existing account onto a digital platform without the need to displace themselves yet without having to repeat their know-your-customer (KYC) process and by protecting the security of access to the digital assets strictly to the actual individual account holder(s) of the account assets, (iii) and reduce the unintended willing or un-willing provisioning of any personal data or account(s) access to malicious individuals or organizations contacting the unsuspecting user(s).

The invention discloses a Holstein cattle production life estimation method and system based on a dynamic regression model, and relates to the technical field of dairy cow breeding, and the method comprises the steps: collecting data of a plurality of Holstein cattle individuals, screening the data of a plurality of Holstein cattle individuals with the life reaching 66 months, and constructing a data set; lactation-related variable data, physiological and environmental variable data and target character MIM66 data when the individuals survive to different age points are extracted from the data set, and the extracted data are coded to form a training set; establishing a linear regression model by taking lactation-related variables, physiology and environment variables as independent variables and taking a target character MIM66 as a dependent variable, training the model by utilizing a training set, and dynamically updating a regression coefficient; and obtaining individual data of the Holstein cattle to be estimated, and estimating the production life character MIM66 of the individual through the dynamic regression model. According to the method, the production life of the Holstein cattle at different age stages can be dynamically and accurately estimated.

Various systems, computer-readable media, and computer-implemented methods of providing improved data privacy, anonymity and security by enabling subjects to which data pertains to remain “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent that is desired—are disclosed herein. Embodiments include systems that create, access, use, store and / or erase data with increased privacy, anonymity, and security—thereby facilitating the availability of more qualified and accurate information. When personal data is authorized by data subjects to be shared with third parties, embodiments described herein may facilitate the sharing of information in a dynamically-controlled manner that also enables the delivery of temporally-, geographically-, and / or purpose-limited information to the receiving party. In one example, the disclosed techniques may be used to functionally separate geospatial information, such that it remains “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent or degree that is desired.

The invention discloses an anti-radiation drug dose data acquisition self-adaptive dose control system. The system comprises a physiological parameter real-time acquisition module, a transfer environment data capture module, a multi-scale pharmacokinetic modeling module, a group Bayesian optimization calculation module, an anti-radiation drug dose adjustment module and a system parameter dynamic updating module. All the modules work cooperatively, the wounded physiological data and the transfer environment data are collected and input into the multi-scale pharmacokinetic modeling module to construct a cross-scale drug metabolism model, the group Bayesian optimization calculation module combines group data optimization calculation, and the anti-radiation drug dosage adjusting module adjusts the dosage according to the optimization calculation. And the system parameter dynamic updating module continuously corrects the parameters. According to the system, cross-scale analysis, group and individual data fusion and dynamic adaptation are achieved, it is ensured that the medicine dosage is accurate and appropriate in the transfer process, and a guarantee is provided for wounded treatment.

The invention relates to a data packet sending method, a data processing unit, a host and a network card, and the method comprises the steps: the host writes an aggregated data packet into a cache unit at a host side, and the data processing unit receives a packet sending notification sent by the host, and obtains a first cache descriptor and a second cache descriptor; the first cache descriptor comprises an address and an aggregation identifier of a cache unit at a host side, and the second cache descriptor comprises the length of each data packet contained in the aggregated data packet; reading the aggregated data packet from the cache unit on the corresponding host side according to the address of the cache unit; extracting each data packet from the aggregated data packet according to the length of each data packet; and sending the extracted data packets to opposite terminal equipment in sequence. According to the method and the device, the bandwidth utilization rate of the PCIe for sending the network packet based on the virtio virtualization protocol can be improved.

Approaches are described for secure identity verification using blockchain technology. A configuration record for a user's identity attributes is obtained. The configuration record is used to evaluate and verify various identity elements through cross-referencing with authoritative sources. Based on the configuration record, identity tokens (KYbits) are generated using cryptographic techniques and stored on a blockchain ledger. These KYbits encapsulate verified identity information and can be utilized in verification processes without disclosing sensitive personal data. The identity tokens enable secure and privacy-preserving transactions across various platforms, ensuring compliance with data protection regulations and enhancing user control over personal identity information.

A computer-implemented method includes receiving a permission setting selection from a user of a vehicle. The permission setting selection is related to preference for processing of personal data of the user. A privacy action rule is generated based on the permission setting selection. In response to a privacy risk event, a privacy action associated with the personal data of the user is executed according to the privacy action rule.

An autonomous machine learning (ML) system and methods are provided that are configured to intelligently cluster training data into separate training data sets for customized ML model training. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform model training operations which include accessing training data, determining a set of features used for the customized ML model training, clustering the training data into the separate training data sets according to the set of features, outputting the separate training data sets, training the plurality of ML models, packaging the plurality of ML models in individual data containers, and configuring the ML data processing platform with the individual data containers.

The present disclosure involves systems, software, and computer implemented methods for data privacy. One example method includes receiving a first request to correct personal data or restrict processing of personal data of a data subject. Response data is identified that is provided by applications in a multiple-application landscape in response to a second request for access to personal data processed by respective applications in a multiple-application landscape. Relevant applications for the first request are identified based on the response data. A data correction or data restriction work package is sent to each relevant application and data correction or data restriction work package responses are received from relevant applications. An overall data correction or data restriction result is determined based on the data correction or data restriction work package responses and is provided in response to the first request.

The invention discloses a personal data value management system and method. The system comprises a main body collaboration layer which is used for defining roles and responsibilities of multiple main bodies and constructing a multi-main-body collaboration framework; the process management and control layer is used for constructing a full-process closed-loop design and establishing linkage rules of all links; the rule guarantee layer is used for constructing a rule system and dynamically adjusting the rule system according to scenes, policies and markets; the value distribution layer is used for carrying out quantitative processing on the individual contribution degree and the enterprise contribution degree and carrying out income distribution according to the individual contribution degree and the enterprise contribution degree; and the personal participation layer is used for constructing a personal participation mechanism so as to obtain personal data dynamics and rights and interests in real time. By adopting the system provided by the embodiment of the invention, a five-layer cooperative personal data valued management system is constructed, each layer not only defines the core function, but also realizes cooperation through data synchronization, rule linkage and a subject counterbalance mechanism, and a management closed loop covering the full life cycle of personal data is formed.

Exemplary systems and methods are disclosed for removing steganography from digital data is disclosed. The method and system involve receiving a digital data. At least one processing device accesses a steganography purifier model. The at least one processing device includes at least a generator configured to scale a magnitude of individual data elements of the digital data from a first value range to a second value range. The scaled data elements are downsampled to remove steganography data embedded in the digital data and produce a purified version. The purified version is upsampled by interpolating new data elements between one or more adjacent data elements to provide an upsampled purified version. The magnitude of the data elements of the upsampled purified version are scaled from the second value range to the first value range to generate a purified output version.

A computer-implemented method for deleting an individual's personal data is provided. A request to delete the individual's personal data may be received from a communication device associated with the individual. The individual's identity may be verified. Responsive to verifying the individual's identity, the request may be compared with a data retention policy, wherein the data retention policy may comprise a data retention end date. If a date of the request is earlier than the data retention end date, a denial notification may be generated and transmitted to the communication device to notify the individual that the request is denied based on the data retention end date. Responsive to a determination that the date of the request is later than the data retention end date, the request to delete the individual's personal data from one or more associated databases may be processed.

The invention belongs to the technical field of child visual training, and particularly relates to a child visual function training system and method based on ciliary muscle adjustment. The child visual function training system based on ciliary muscle adjustment comprises a collaborative acquisition module and a dynamic training module. The collaborative acquisition module acquires an anterior segment image of an eyeball in real time through a built-in image acquisition unit so as to identify eye features, acquires extraocular muscle pressure deformation data through a built-in pressure acquisition unit, and then associates and analyzes the eye features and the extraocular muscle pressure deformation data through a built-in data fusion unit; a mapping model is established according to the analysis result, the refractive power change of the crystalline lens is calculated, and the calculation result is processed into the ciliary muscle adjustment amount; the dynamic training module determines training parameters in combination with child individual data and historical data acquired by the collaborative acquisition module, and dynamically adjusts training duration and light wave parameters according to the ciliary muscle adjustment amount, thereby solving the problems of poor child training effect and safety risk in the training process.

The invention provides a cross-individual human body behavior recognition method based on self-training and active inquiry, and the method comprises the steps: obtaining a source domain data set with a label and a target domain data set without a label; processing acceleration data in the source domain data set with the label and the target domain data set without the label; dividing the target domain data into a label-free training set and a test set in proportion; establishing a feature extractor based on a dual-channel convolutional network, and extracting features of time and space dimensions from the input two-dimensional acceleration data at the same time; a cross-individual adaptation algorithm based on a confidence threshold value, sparse query and label propagation is adopted, a model is trained on a non-label training set on a target domain, and the adaptation problem caused by cross-individual data distribution difference is relieved. According to the method, the behavior recognition accuracy of the model in a cross-individual scene can be improved, the development cost and the user burden are reduced, and a more intelligent and adaptive solution is provided for application of wearable equipment such as exoskeleton robots.