132 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 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 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.

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.

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.

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 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

The invention provides a data desensitization method based on bastion host operation and maintenance, a computer program product, electronic equipment and a storage medium, and the method comprises the steps: obtaining communication traffic of bastion host operation and maintenance; analyzing the communication flow to obtain standardized format data; inputting the standardized format data into a pre-constructed large model for sensitive data identification to obtain sensitive data and a corresponding sensitive degree; querying a pre-constructed permission desensitization strategy mapping table according to the sensitive data and the corresponding sensitive degree to obtain a desensitization strategy; and desensitizing the sensitive data according to the desensitization strategy. By implementing the application, accurate classification and identification of sensitive data can be realized, different desensitization strategies can be carried out according to different protocol types, various fine-grained desensitization can be realized, desensitization can be carried out according to operation and maintenance rights, and the desensitization process is more flexible and controllable.

Embodiments of the present disclosure relate to customized machine learning training of radiotherapy clinics. Disclosed herein are methods for selecting and preparing patient data to facilitate the employment and custom training of machine learning models in a clinical environment, particularly for radiotherapy treatment planning. The disclosed embodiments simplify the customized training process through an automated workflow that includes pre-filtering patient metadata, retrieving related DICOM files, optional data anonymization, and generating training data. And organizing the data 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 the clinical workflow, enabling clinics to train and implement predictive models replicating specific clinical practices, thereby improving treatment accuracy and improving patient outcomes.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for anonymizing unstructured data. In some implementations, a server can receive unstructured data. The server can automatically detect attributes in the unstructured data using a trained machine-learning model and can determine an amount of undetected attributes and detected attributes in the unstructured data. The server can simulate additional attributes for the unstructured data according to the amount of undetected attributes. The server can analyze a risk of disclosure in the unstructured data using the detected attributes and the simulated additional attributes. The server can modify the detected attributes according to the analyzed risk of disclosure and replace the detected attributes with the modified detected attributes in the unstructured data.

The invention discloses an information feedback file classification method, system and equipment based on a large model and a medium, belongs to the technical field of large models, and aims to solve the technical problem of how to efficiently and quickly classify the content of feedback files, realize quick identification and accurate classification of the feedback files and improve the classification efficiency of the feedback files. According to the technical scheme, the method comprises the steps of information feedback file collection and data aggregation, wherein an information feedback file is obtained through a service related party, and an excel data aggregation file is formed in a set format; data desensitization: obtaining original description and category classification of the information feedback file according to the excel data aggregation file, and carrying out sensitive information replacement processing on the original description through a Deepseek-V3 large model to obtain desensitized original description data; model fine tuning: performing fine tuning on the Qwen2.5-14B model by adopting a multi-round dialogue format in a Lora mode, inputting original description into the Qwen2.5-14B model, and outputting a classification result by the Qwen2.5-14B model; model deployment: performing model deployment reasoning through an artificial intelligence processor chip; and generating a classification result.

This invention relates to the field of data anonymization technology and provides a dynamic data anonymization method for electronic data, comprising the following steps: Calculating a query intent convergence score based on a received electronic data access request. In this invention, upon receiving an electronic data access request, the field range, filtering conditions, aggregation behavior, and join depth of the query statement are first analyzed, and a convergence score is calculated. The score directly maps to the granularity of field anonymization, ensuring that the degree of anonymization closely matches the query intent and avoiding excessive masking that could distort the results. Subsequently, external connectivity paths are traced based on quasi-identifiers and asset graphs. Information arbitrage risk is quantified according to multiplicative probability and sensitive value. If the risk exceeds the threshold, a cutoff command is immediately triggered, and high-risk fields are immediately isolated to prevent cross-domain assembly.

The invention discloses a privacy protection-oriented anonymized data watermarking method and device and a medium, and relates to the field of privacy protection, and the method comprises the steps: encrypting to-be-embedded original watermark information, and generating encrypted watermark information; performing error correction coding on the encrypted watermark information to obtain a watermark coding sequence, and converting the watermark coding sequence into a Gray code sequence; sorting the data records in the original anonymized data set based on preset attributes to obtain a plurality of record groups; based on the segmentation numerical value of the Gray code sequence, determining the number of records needing to be modified in each record group; and randomly selecting the data records corresponding to the number, and modifying the selected data records to generate the anonymized data containing the watermark. By combining cryptographic encryption, error correction coding and a data modification mechanism keeping anonymization, reliable traceability of illegal distributors is realized on the premise of ensuring that the data anonymization level is not reduced, so that an effective balance mechanism is established between data sharing and privacy security.