145 results about "Data anonymization" patented technology

Systems and methods for large language model (LLM)-agnostic data anonymization. Data anonymization includes data obfuscation (and data deobfuscation) to protect confidential information a user is going to send to an LLM service or application programming interface (API). Encryption can be used for data obfuscation and particularly, for securing data from unauthorized access. Likewise, decryption can be used for data de-obfuscation.

The invention provides a low-altitude industry chain information intelligent cooperation and airspace management platform based on federal learning. Through cluster ad hoc network and decentralized federated learning, the method is divided into three stages of data acquisition, data preprocessing and analysis application, low-altitude scene data communication of dynamic topology and static topology is realized, and the efficient communication requirement in a low-altitude operation scene is met. According to the system, differential data transmission strategies are adopted, optimization design is carried out on a single machine, a co-task cluster, a cooperative cluster and a non-cooperative cluster, and the communication efficiency and the resource utilization rate are improved. In the data preprocessing stage, federated learning aggregation of multi-source data fusion, data anonymization and decentralization is combined, and data privacy protection and safe sharing of three parties of industry, university and research are ensured. In the analysis and application stage, low-altitude aircraft task scheduling, airspace flow control and environmental condition evaluation are realized through aircraft analysis, airspace analysis, environmental analysis and digital twinborn technologies.

The invention provides an anonymization processing method and system for emotion data in a vehicle. The anonymization processing method comprises the steps of collecting multi-mode emotion data; correspondingly carrying out local preprocessing and multi-modal alignment processing on the multi-modal emotion data; corresponding sensitive emotion feature information in the preprocessed multi-modal emotion data is extracted through a lightweight recognition algorithm, and the sensitive emotion feature information at the recognized position is packaged in a unified mode; performing desensitization processing on various types of emotion modal data, and encapsulating and synchronizing desensitization results with labels; deep emotional feature extraction is performed on the image, the voice and the physiological signal through a multi-modal fusion model, and the extracted three types of deep features are fused to obtain an emotional representation vector; and inputting the emotion representation vector into a pre-trained emotion recognition model, and outputting the current emotion state of the passenger, including the specific emotion category and the corresponding confidence coefficient. According to the method, emotion analysis and transmission are performed after data anonymization is realized, and accurate judgment of the system on the emotion state is not influenced while privacy security of the user is ensured.

A tokenization system receives a request for data anonymization, the request referencing structured data containing values of interest. Responsively, the tokenization system performs a tokenization operation on the structured data, generates a corresponding token, and replaces a value of interest with the corresponding token to produce an anonymized version of the structured data. The tokenization system stores the value of interest with the corresponding token in a secure data vault. Subsequently, the tokenization system may receive a request for revealing the anonymized version of the structured data containing the corresponding token. In response, the tokenization system can perform a reveal operation on the anonymized version of the structured data by querying the secure data vault for the corresponding token and retrieving the value of interest from the secure data vault using the corresponding token. The reveal operation produces a detokenized version of the structured data containing the value of interest.

A device, method, and system for treating data from legacy infrastructure is disclosed. Illustratively, the device memory stores computer executable instructions that when executed by the processor cause the processor to provide a dataset comprising a plurality of characters and provide a token table for tokenizing datasets. The token table includes mappings that define replacement tokens for characters in datasets. The instructions cause the processor to generate a tokenized dataset based on the dataset by (1) for each contiguous sequence of letter characters of the plurality of characters, determining a respective letter token having the same length as the respective contiguous sequence, and (2) generate the tokenized dataset by replacing each contiguous sequence of letter characters of the plurality of characters with the determined respective letter token.

Systems and methods for large language model (LLM)-agnostic data anonymization. Data anonymization includes data obfuscation (and data deobfuscation) to protect confidential information a user is going to send to an LLM service or application programming interface (API). Encryption can be used for data obfuscation and particularly, for securing data from unauthorized access. Likewise, decryption can be used for data de-obfuscation.

A method is provided for controlling traffic management infrastructure, comprising: capturing images of vehicle license plates converting the images into license plate data including license plate characters; transmitting the license plate data to a central server having a data collection module, a data anonymization module, a data storage module and a data analysis module; extracting the license plate characters from the license plate data; packaging the license plate characters in license plate files; anonymizing the license plate files; storing, by the data storage module, the anonymized license plate files; analyzing the anonymized license plate file to identify a traffic condition; and outputting at least one command to at least one component of traffic management infrastructure, thereby causing the component to adjust an aspect of traffic management based upon the identified traffic condition.

The invention discloses a data anonymization adjustment method and system based on dynamic association risk analysis. The method comprises the following steps: acquiring all original data associated with two types of systems forming a cross-domain scene in a preset period; extracting a first domain feature and a second domain feature with time correlation and space correlation from all the original data; obtaining the time matching rate of the two types of features based on the time window overlapping frequency; acquiring spatial relevance of the two types of features based on an external map knowledge base; obtaining semantic relevance of the two types of features; carrying out weighted fusion on the three types of relevance to obtain a relevance edge weight, and determining a path risk level formed by the two types of features according to the weight; and dynamically calling a corresponding anonymization strategy according to the path risk level, and executing the strategy on the corresponding data. According to the method, the problems of leakage risk and the like caused by cross-domain association of the weakly anonymized data set can be effectively solved, and the balance of dynamic risk perception and anonymization level self-adaption is realized.

A data anonymization technique for datasets including personal identifiable information (PII) such as utility datasets may primarily include assigning anonymous identifiers to nodes in the dataset and swapping or otherwise moving portions of information between nodes of the dataset. The methodology of the swapping or moving operation may vary optionally based on a number of parameters, and may include swapping endpoints under a single parent, swapping endpoints between similar parents, and / or swapping similar endpoints between parents. The anonymization technique may output an anonymized dataset which reflects a topology of the original dataset and may optionally be updatable and modifiable to include additional data about existing or new nodes.

The invention discloses a method and a system for evaluating an anti-fraud index. The method comprises the following steps of: 1, acquiring and processing multi-source heterogeneous data; the multi-source heterogeneous data comprises telecommunication data, a social platform, historical fraud data, a user portrait and a system log; the data processing comprises text cleaning, voice-to-text conversion, image feature extraction and data anonymization; 2, calculating personal risk features, regional risk features and system efficiency features; 3, comprehensively evaluating an anti-fraud index based on the personal risk characteristics, the regional risk characteristics and the system efficiency characteristics; the method is based on a multi-dimensional index system of a personal risk index (PRI), a regional threat index (RTI) and a system efficiency index (SEI), and provides comprehensive support of accurate prevention and control, dynamic response and scientific decision for an anti-fraud system.

Identity nodes within bidirectional dual node hashchain (BDNH) bundles generate hash keys by processing partial metadata and linking them through identity graphs. Translation nodes perform identity resolution, data anonymization, and re-keying by transforming original identifiers into anonymous IDs and re-key IDs. Laplace noise enabler capsules introduce Laplace noise into translation layer logs for differential privacy. BDNHs share metadata between adjacent bundles for secure and efficient data transmission. A cognitive analytics layer shared among all bundles verifies hashed encrypted data packets using a gossip protocol for real-time validation. An analytics workspace layer activates re-key IDs and creates digital tags for datasets using BERT transformers, linking data through knowledge graphs. The system manages parallel processing and includes machine learning modules, homomorphic encryption, federated learning, zero-knowledge proofs, attribute-based encryption, and anomaly detection. Bundles' AI engines work with the Laplace noise enablers to analyze data, optimize noise, and enhance the data clean room functionality.

The invention relates to the field of wisdom education, in particular to a data protection method for a wisdom classroom, and the method comprises the steps: carrying out the transmission encryption, storage encryption and end-to-end encryption of teaching data based on a hybrid encryption technology; user permission is managed through multi-factor authentication and a role-based dynamic access control mechanism; the student privacy is protected by adopting data desensitization, differential privacy and anonymous ID generation technologies, and student information de-identification is realized; localized data processing and storage are completed by utilizing the edge computing node, and cloud dependence is reduced. Through multi-level encryption, dynamic authority control, data anonymization and localization processing technologies, the security of teaching data and user privacy are comprehensively guaranteed, the risk of data leakage is significantly reduced, the compliance and security of education data processing are improved, and the method is suitable for various smart classroom scenes.

The invention relates to the field of high-altitude object throwing detection and early warning, and discloses a high-altitude object throwing prevention detection and early warning system based on an intelligent building, and the system comprises a data collection and processing module which obtains a physical structure factor, a behavior mode factor and an environment state factor through collection and calculation; the risk early-warning module is used for eliminating abnormal values of the risk data, calculating risk early-warning values, comparing the risk early-warning values with a threshold value, dividing risk levels, executing an early-warning strategy according to the risk levels, analyzing and optimizing the data by using a model, and dynamically correcting the early-warning values; the dynamic optimization module is used for smoothing short-term fluctuation through a calibration formula by using a historical risk early warning value; calculating an early warning accuracy rate, and if the early warning accuracy rate is lower than a threshold value, dynamically adjusting a preset proportionality coefficient to minimize an early warning error rate; and the privacy processing module is used for guaranteeing the privacy of the resident in a whole process by adjusting the shooting range of the camera, utilizing an image data set training model, carrying out anonymization encryption on acquired data, uploading aggregation parameters and combining the training model and fuzzy position information during early warning.

The invention discloses a big data security protection and information analysis system and method in an Internet of Things environment, and the method comprises the steps: an Internet of Things device collects data, transmits the data to a storage module through encryption, carries out the encryption processing of the data through a hybrid encryption technology, and carries out the hierarchical storage according to the sensitivity; the system uses multi-factor authentication to verify the legality of the device, and adjusts the access authority according to the behavior of the device; in the data storage and analysis process, homomorphic encryption and data anonymization technologies are applied; the state of the data flow is monitored in real time, abnormity and potential threats are identified, and a processing strategy is dynamically adjusted in combination with intelligent analysis; when a security threat is detected, unauthorized equipment is automatically blocked, and abnormal data flow propagation is prevented; according to the big data security protection and information analysis system and method in the Internet of Things environment, through a multi-level security protection mechanism, it is ensured that big data analysis of the Internet of Things is safe and efficient, and the intelligent decision-making ability and the application efficiency are improved.

A method and system for anonymizing data to be transmitted to a destination computing device is disclosed. Anonymization strategy for data anonymization is provided. Data to be transmitted is received from a user computer. Selective anonymization of the data is performed, based on the anonymization strategy, using an anonymization module. The data includes a plurality of characters. A portion of the anonymized data is selected as a search ID. A cross reference between a search key indicative of a portion of the received data and the corresponding search ID is stored.

The invention discloses a highway toll passage management and control system based on a cloud toll architecture, which comprises a cloud intelligent billing platform, an edge decision node and a vehicle-mounted trusted terminal, and is characterized in that the cloud intelligent billing platform, the edge decision node and the vehicle-mounted trusted terminal are connected through signals; the cloud intelligent charging platform comprises an elastic rate engine, a privacy settlement middle station and an emergency visualization system, and the edge decision node comprises a heterogeneous communication unit, a security authentication unit vehicle and a lane optimizer. The LSTM model is combined with real-time data, the rate prediction error is smaller than or equal to 2.5%, compared with a traditional linear model (the error is about 8%), the ETC and C-V2X switching success rate is remarkably optimized to be larger than or equal to 99.5%, the passing efficiency is improved by 30%, and data anonymization processing consumes time lt through the differential privacy technology; and the block chain clearing efficiency reaches 5000 pieces per second in 100ms.

Disclosed herein are methods for selecting and preparing patient data to facilitate the adoption and customized training of machine-learning models in clinical settings, particularly for radiation therapy treatment planning. The disclosed embodiments streamline the customized training process through an automated workflow that includes prefiltering patient metadata, retrieving relevant DICOM files, optional data anonymization, and generation of training data. The data is then organized into a format suitable for machine-learning training. The embodiments discussed herein reduce manual labor, minimize errors, and accelerate the integration of machine-learning into clinical workflows, enabling clinics to train and implement predictive models that replicate specific clinical practices, thereby enhancing treatment precision and improving patient outcomes.

This invention relates to the field of cross-border data security transmission, providing a method and system for cross-border VPN data anonymization transmission adapted to multi-regional compliance. The method includes: parsing the header of the original cross-border data packet to generate data flow context metadata; loading a set of compliance constraints and a task utility objective function accordingly; scanning the data payload in parallel to generate a pre-analysis feature set; constructing decision context information; instantiating the objective function and transformation strategy space; solving for the optimal transformation strategy through constraint optimization; compiling it into an optimal anonymization execution plan; executing the optimal anonymization execution plan to generate a new data feature set; combining it with non-sensitive data to reorganize and inject a new data payload; outputting a second cross-border data packet; encapsulating it into a third cross-border data packet and generating a decision audit log; comparing the compliance decision and audit label with the target end's local compliance policy to trigger a policy update. This invention integrates multi-regional compliance constraints and business utility objectives to construct an intelligent anonymization and audit closed-loop mechanism for cross-border data.

The present disclosure relates to data anonymization methods, apparatuses, and storage systems. In one example method, a storage system obtains a data access request, obtains target data based on the data access request, and performs anonymization processing on the target data to obtain anonymized target data. Then, the storage system sends the anonymized target data.

The invention relates to the technical field of medical data privacy protection and information security, in particular to a dynamic medical data anonymization processing system and method based on message monitoring. The system comprises a configuration management module, a message monitoring module, a cache management module, a refreshing cache module, a dynamic data calling module, an anonymization rule execution module and a verification and auditing module. According to the method, a message monitoring mechanism is adopted, change events related to medical data are captured in real time, and anonymization rules are adjusted in time; by adopting an anonymization strategy defined by multiple types of annotations, the privacy protection requirements of complexity and diversification of medical data can be met; the performance of anonymization processing is optimized in combination with delayed loading and a functional programming mode, and the efficiency and compliance of data processing are ensured; by verifying the data after anonymization processing, it is ensured that the processed data can meet the requirements of laws and regulations, and the integrity and analysis value of the data are maintained while the privacy of the patient is protected.

This invention relates to a privacy data sharing method based on blockchain and privacy-preserving computation, belonging to the field of blockchain technology. The data owner performs local encryption on the privacy data to be shared, and combines this with data anonymization and format standardization preprocessing to generate encrypted data and identification information. A smart contract is deployed through the blockchain network to write the encrypted data's summary, access control policies, and data index metadata into the blockchain. The data requester, based on the data index on the blockchain and its own needs, initiates a joint computation request through the smart contract, and undergoes identity verification and permission review. After permission review is passed, the encrypted data is jointly analyzed. Upon completion of the analysis, the smart contract automatically records the computation result summary and securely returns the joint computation result to the data requester. Based on the data traceability and automatic destruction mechanism triggered by the smart contract, all intermediate data, keys, and encrypted fragments are automatically deleted.

The invention discloses a data anonymization processing method and system based on differential privacy, and relates to the field of data anonymization processing, and the method comprises the steps: receiving a data retrieval request of a data user; retrieving from an original database to obtain an initial data set, and constructing a differential privacy parameter search space; randomly determining a first differential privacy parameter in the differential privacy parameter search space, and performing data anonymization processing on the initial data set; performing data privacy evaluation and data availability evaluation on the first to-be-evaluated data set to obtain a first data privacy degree and a first data availability degree, and generating a first parameter fitness; performing parameter optimization based on the first parameter fitness and the first differential privacy parameter, and determining an optimal differential privacy parameter; and performing data anonymization processing on the initial data set to obtain a target data set, and feeding back the target data set to a data user. The problems that in the prior art, data privacy leakage cannot be quantized, reliable privacy guarantee is difficult to provide, and dynamic adaptability of differential privacy parameters is poor are solved.

This invention discloses a knowledge-based intelligent interpretation system and method for laboratory reports, comprising four sequentially linked modules: a data input module for receiving laboratory data transmitted from the LIS system, the data including patient laboratory indicators and basic patient information, supporting data anonymization, and the basic patient information including at least age, gender, pregnancy status, medical history, and family medical history. This invention effectively reduces patient consultations, allowing patients to understand their reports immediately, reducing anxiety, significantly improving patient satisfaction, avoiding inconsistent interpretations among doctors, enhancing the ability of general practitioners in community hospitals to interpret laboratory reports, and reducing unnecessary duplicate examinations through accurate interpretation.

The invention discloses a data anonymization transmission method, system and device based on data hosting, and relates to the data anonymization transmission system based on data hosting, which comprises a data party privacy computing platform used for hosting data of a data party obtained after data desensitization processing; wherein the data desensitization processing is to clean sensitive data according to an encryption rule of a data party; the data operation trusteeship platform is used for trusteeship of the encryption rule of the data party and pushing the encryption rule of the data party to the demand party privacy computing platform; and the demand side privacy computing platform is used for encrypting sensitive information in data of the demand side according to the encryption rule of the data side, storing a plaintext and ciphertext mapping relation table, decrypting a computing result obtained by the data side privacy computing platform according to the plaintext and ciphertext mapping relation table, and transmitting the decrypted computing result to the demand side privacy computing platform. And the demand side obtains a final decryption result.

A data anonymization pipeline system for managing holding and pooling data is disclosed. The data anonymization pipeline system transforms personal data at a source and then stores the transformed data in a safe environment. Furthermore, a re-identification risk assessment is performed before providing access to a user to fetch the de-identified data for secondary purposes.

The application discloses a kind of campus health management system and method based on group benchmark analysis, comprising: data acquisition module, for collecting the health and behavior data of student individual, and implementing data anonymization processing;Data processing and analysis module, including group clustering unit, for clustering the anonymized student individual data into homogenization analysis group according to student group attribute;Benchmark generation unit, for calculating the dynamic statistical benchmark band of one or more health indicators in each analysis group based on historical data within the set time window;Individual comparative analysis unit, for comparing each student individual data with the dynamic statistical benchmark band of corresponding health indicators of its analysis group, and calculating the relative deviation of individual health indicators;Early warning and report generation module, for triggering hierarchical early warning according to individual comparative analysis result, and generating visual health report. It can be widely applied to the field of campus health management technology.

The embodiment of the invention discloses a data desensitization processing method and device, a storage medium and electronic equipment, and relates to the technical field of computers. In an embodiment of the present specification, a proxy device receives a data write request from a transmitting device and for a receiving device, the data write request being used to instruct to write initial data to the receiving device. Furthermore, the proxy device carries out desensitization processing on the initial data according to the desensitization processing strategy to obtain desensitization data, the desensitization processing can enable sensitive information in the initial data to be processed through replacement, hiding and the like on the premise that the initial value of the initial data is not lost, so that real information cannot be exposed, and therefore privacy and safety of the initial data are ensured. Further, the proxy device sends a data write-in request carrying the desensitized data to the receiving device so as to write the desensitized data into the receiving device.

The invention relates to the technical field of data security, and discloses a data desensitization method and system. The method comprises the following steps: analyzing a data access request of a user, and determining a target data structure; based on the target data structure, a target classification and grading label is determined, and the classification and grading label is generated by analyzing the initial service information and the system data source by using a large model in advance and is matched with the corresponding data structure in advance based on a preset matching rule; a target desensitization strategy is determined based on the target classification and grading label, and the desensitization strategy is determined by analyzing the classification and grading label of the corresponding data in the system data source through a large model in advance; obtaining to-be-accessed data based on the data access request; and performing desensitization processing on the to-be-accessed data by using the target desensitization strategy to obtain desensitized data, and sending the desensitized data to the user. According to the technical scheme, the data classification and grading efficiency and the desensitization strategy making accuracy can be improved.

To solve a problem which arises when a diary is automatically created from data in an application or conversations with an assistant application.SOLUTION: A system includes: means for automatically creating a diary from data in an application or conversations with an assistant application; means for personal utilization and anonymization of collected data; and means for an SB to exclusively utilize the data.SELECTED DRAWING: Figure 1

To solve the following problems of a navigation system in a facility, such as inaccurate indoor GPS signals, lack of personalized information provision, insufficient e-commerce experience, low reliability of information provision, and inadequate protection of user privacy and data security, resulting in significant degradation of quality of experience of visitors and detracting the appeal of the facility.SOLUTION: The present invention provides accurate position information in a facility using Wi-Fi and NFC technologies, and provides personalized information using generative AI. The invention improves accuracy and reliability of information by combining ensemble learning and multimodal input, to ensure user privacy and data security through anonymization or encryption of data. Accordingly, rich and safe on-site experience for users can be provided and overall service quality improvement can be implemented.SELECTED DRAWING: Figure 1