28 results about "Pseudonymization" patented technology

A taxonomy-agnostic data protection framework includes a plugged-in data classification taxonomy definition having a taxonomy identifier and a set of data classification indicators. The taxonomy-agnostic data protection framework also includes a plugged-in set of data classification processing routines, and a plugged-in mapping mechanism which maps between data classification processing routines and data classification indicators. The framework facilitates efficient, accurate, and thorough implementation of data classification propagation per the plugged-in taxonomy, both within a given program and between programs that connect over a network. The framework also facilitates flexible implementation of per-taxonomy data protection actions such as deletion, redaction, encryption, anonymization, pseudonymization, hashing, or enrichment, in response to individual or combined data classification indicators. Static analysis of annotated source code determines whether data classifications are accurately and comprehensively propagated with a program.

The invention provides a high-efficiency self-adaptive microdata desensitization method and system for enabling a large language model, which solve the problems of high data desensitization cost and the like, and comprises the following steps of: S1, identifying and classifying privacy information, identifying field types in a data table, and distinguishing a direct identifier DI, an undetermined direct identifier PDI, a quasi identifier QI and a privacy risk-free field; s2, performing field association analysis, namely analyzing an association relationship between the DI field and the QI field; s3, performing generalization desensitization on the QI, and performing generalization desensitization on the selected QI field to realize K-anonymity; s4, correcting the QI desensitization amp according to the correlation between the fields; carrying out DI pseudonymous desensitization, repairing privacy vulnerabilities caused by logic association among fields participating in generalization desensitization by combining the use of a large language model, and desensitizing QI and DI fields which do not participate in generalization on the basis of a desensitization result after vulnerability repairing; and S5, outputting desensitization data, integrating desensitization results, and generating a final data set. The method has the advantages of good data desensitization effect, high availability and the like.

The present invention relates to a method for processing sensitive data, particularly biomedical images, securely, automatically, and reproducibly on a cloud computing infrastructure. The invention also discloses the device for implementing this method. The invention relies in particular on cloud computing, cryptography, biomedical imaging, pseudonymization, anonymization, and advanced signal and image processing technologies. The invention also covers a use case for such a method through the secure implementation of image processing technologies (a business application) applied to biomedical images. In one embodiment, these images are obtained from magnetic resonance imaging (MRI), specifically for applying advanced processing with the business application to map the apparent transverse relaxation rate (R2*) and perform quantitative susceptibility imaging (QSM).Figure for the abbreviation: figure 1.

A method for applying pairwise differential privacy to variables in a data set is disclosed. The method includes designating a random instance seed value to a first data set variable in an original data set and designating the random instance seed value to at least one additional data set variable in the original data set if a high degree of correlation is identified between the first data set variable and the at least one additional data set variable. The method further includes determining an adaptive sensitivity parameter corresponding to the first data set variable and utilizing, by a noise generation manager, two or more among the first data set variable, the random instance seed value, and / or the adaptive sensitivity parameter to generate and apply additive noise to the first data set variable to produce a pseudonymized variable for inclusion in a pseudonymized data set associated with the original data set.

The embodiment of the invention provides a chronic disease data management method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-layer data access permission control system, achieving the access control of a vertical dimension from a database level to a field level, and constructing a permission range of a horizontal dimension based on roles and functions; an access subject attribute, a data resource attribute and an environment attribute are defined as decision factors, and a compliance assessment result is mapped into a specific permission policy rule. For different application scenes, the system can automatically select and combine desensitization technologies such as field shielding, data generalization, random perturbation, pseudonymization, aggregation statistics and k-anonymization, and safe sharing of data is achieved. According to the embodiment of the invention, through RDF multi-framework context dependence compliance evaluation, attribute-driven access control and scene-aware data desensitization, the data value mining and privacy protection requirements are effectively balanced, and technical support is provided for chronic disease accurate management and medical data safe circulation.

The present invention relates to a method for processing sensitive data, particularly biomedical images, securely, automatically, and reproducibly on a cloud computing infrastructure. The invention also discloses the device for implementing this method. The invention relies in particular on cloud computing, cryptography, biomedical imaging, pseudonymization, anonymization, and advanced signal and image processing technologies. The invention also covers a use case for such a method through the secure implementation of image processing technologies (a business application) applied to biomedical images. In one embodiment, these images are obtained from magnetic resonance imaging (MRI), specifically for applying advanced processing with the business application to map the apparent transverse relaxation rate (R2*) and perform quantitative susceptibility imaging (QSM).Figure for the abbreviation: figure 1.

The present invention relates to a method for adding data to a database, wherein the method comprises retrieving a transport pseudonym based, at least in part, on a medical data code assigned to a user, and retrieving a medical data pseudonym based, at least in part, on the transport pseudonym. The present invention also relates to a corresponding system, to a use of the system, and to a computer program product for generating the medical data code.

A sentence processing device according to the present disclosure comprises: a mask means for masking a proper noun included in a text sentence; a prediction means for predicting, by using a pre-trained model for natural language processing of the text sentence, a word to be pseudonymized into a masked portion; a replacement means for replacing the proper noun with the predicted word; an execution means for executing, by using a specialized-type trained model obtained by specializing the pre-trained model for a specific task, natural language processing of the text sentence in which the proper noun has been replaced; a restoration means for restoring, to the original proper noun, the word that has been replaced according to the processing result; and an output means for causing the restored processing result to be outputted.

Privacy-compliant proxy system for large language models, comprehensive: a) an authentication module for validating user access using API keys, JWT tokens or cookies with role-based access control, b) a multi-layered PIl recognition module with at least four parallel recognition layers, comprising a neural named entity recognition layer, a rule-based regex recognition layer, a keyword-based recognition layer, and a context-based recognition layer, c) a pseudonymization module for replacing identified personal data with type-preserving pseudonyms with session-consistent assignment and randomized starting counters as an anti-cardinality measure, d) a multi-provider proxy module for forwarding pseudonymized requests to at least one external AI language model provider, e) a de-pseudonymization module for retranslating the pseudonyms in the AI ​​response and a response PII redaction module for detecting and redacting AI-generated new personal data in the response, characterized in that f) a hybrid signature and audit chain module is provided which generates an audit entry for each transaction, signs it with both a classical signature method (Ed25519) and a post-quantum secure signature method (ML-DSA-65), and links the entries in a hash chain, with each signature being verified immediately after generation and before being written to the database (write-time verification), and g) a zero-knowledge encryption module is provided which encrypts the audit entries after signing with a user-specific Data Encryption Key (DEK), the DEK being derived from the user password and existing only in volatile memory during the active session, and the hash chain and signatures being preserved in plaintext so that the integrity of the audit chain can be verified without decrypting the contents.

In a first aspect, the invention relates to a computer-implemented method for supporting a treatment of a cancer patient. The patient is provided with information about cancer treatment and related guidelines. During the treatment process, patient data is collected through a module via a user interface. This data is then pseudonymized to ensure privacy and stored on a server. The stored data is subsequently analysed, and based on this analysis, a treatment recommendation is provided at the user interface to assist in the patient's ongoing treatment. In a further aspect, the invention relates to a computer program product.

The present disclosure relates to a liveness identify in a machine-to-machine (M2M) system, and a method for operating an M2M device may include identifying a protection regulation applied to data, identifying a protection mechanism applied to the data, and transmitting a resource creation request message including information indicating the protection regulation and the protection mechanism to a second M2M device, and the data may include personal information or privacy information that is generated by an application executed in the M2M device.

Offline-capable documentation system for the social pedagogical and social work sector, comprehensive a) a speech input unit that can be executed locally on a mobile or stationary device to convert spoken language into text without an Internet connection, b) a locally embedded AI module for automatic linguistic smoothing, neutralization and pseudonymization of the transcribed text, c) a rule-based assignment logic that automatically recognizes and assigns areas of life, measures, methods and objectives from the prepared text using predefined technical rules and keyword lists, d) a local storage unit for the encrypted storage of the processed and categorised documentation entries, (e) a user interface for entering the speech input, checking the processing results and outputting the final documentation.

Computer-aided system for the pseudonymized analysis of individual innovation characteristics, including: • a data acquisition module (1) with a web-based user interface for entering test data and questionnaires via encrypted connections, • a pseudonymization module (2) with a hash algorithm for the irreversible conversion of identification data into eight-digit alphanumeric identifiers using a salting function, • an AI analysis module (3) with API-based integration of machine learning methods for automated classification of input data, wherein explainable AI mechanisms provide comprehensible explanations of the analysis results, • a reflection module (4) with a database-based dashboard for interactive visualization of the analysis results using configurable diagrams, • a communication module (5) for encrypted anonymous messages between pseudonymized user identifiers and research accounts, wherein the system implements role-based access control and processes personal data exclusively in pseudonymized form.

This invention discloses a local reversible pseudonymization privacy protection method and system for cloud-based intelligent agent memory, relating to the fields of artificial intelligence and data security technology. The invention pre-configures a four-level privacy classification system on the local device, performs edge-side privacy detection and hierarchical recognition on user-input text, replaces sensitive information with placeholders carrying semantic types, and uploads it to the cloud. The cloud uses these placeholders to perform semantic reasoning, tool invocation, and memory archiving. The local device then restores the original sensitive information from the placeholders returned by the cloud, achieving a privacy protection effect where plaintext privacy data remains locally while the cloud only processes semantically abstract information. This invention can block privacy leakage paths at the source without compromising the semantic understanding and long-term memory capabilities of cloud-based intelligent agents, balancing privacy security and system utility. It features low processing latency and is imperceptible to the user, making it suitable for long-term interaction and memory management scenarios of cloud-based intelligent agents driven by large language models.

The invention relates to a method for managing information associated with a subject identified by identification data (I), wherein a client (C), for the purpose of accessing data assigned to the identified subject: a) generates, according to specified criteria, or makes available identification data relating to the subject, b) generates or makes available at least one key (R), c) optionally prepares the identification data for encryption using the key, d) encrypts the identification data or data derived therefrom using the at least one key and generates an encryption result (S), the encryption process ensuring that the identification data, which is prepared for encryption if necessary, or data derived therefrom can be recovered from the at least one key or a key (RPr) assigned to said key and from the encryption result by means of a decryption process, and e) transmits the encryption result and, if necessary, the at least one key in the form of an account request to a service network comprising at least two separate services, the individual services of the service network: f) not granting one another access to the keys and / or encryption results transmitted to the services and potentially stored thereon.

To conceal sensitive information so that the possibility that sensitive information values are inferred (re-identified) is reduced, and analyze information.SOLUTION: A trusted execution environment unit 102 (TEE unit) includes a pseudonymization processing unit 123 and a probabilistic encryption processing unit 124 in TEE. The pseudonymization processing unit 123 converts sensitive information 161 into pseudonymized information 163. The probabilistic encryption processing unit 124 in TEE performs encryption processing on a combination of a random number 164 and pseudonymized information 163 to generate secure sensitive information 165. A data analysis platform unit 103 (DAP unit) includes a probabilistic decryption unit 132 in DAP and a data analysis unit 133. The probabilistic decryption unit 132 in DAP performs decryption processing on the secure sensitive information 165 to generate pseudonymized information 163. A data analysis unit 133 performs analysis using one or both of pseudonymized information 163 and information 162 other than the sensitive information.SELECTED DRAWING: Figure 1

Techniques relating to data privacy are disclosed. These techniques include identifying data fields relating to a first data record, and generating transformed data fields by transforming the data fields using one or more transformations. Each of the transformations includes at least one of: (i) a hash function or (ii) a surrogate key function. The techniques further include generating encrypted data fields by encrypting the data fields using a respective encryption key, and storing the transformed data fields, the encrypted data fields, and of encryption keys. This includes storing the transformed data fields in one or more first storage locations, storing a first mapping between each of the transformed data fields and a corresponding encrypted data field in one or more second storage locations, and storing a second mapping between each of the transformed data fields and a corresponding encryption key in one or more third storage locations.

According to the metabolic acidosis-oriented drug administration effect evaluation method and system, localized data source nodes are deployed in a medical institution, patient diagnosis and treatment data are safely stored and processed, data privacy is ensured, a unified federal data model and a semantic interoperability layer are established, and the evaluation efficiency is improved. The consistency of cross-mechanism data in structure and semantics is realized; an access control and auditing mechanism is constructed based on a distributed account book technology, an intelligent contract is used for managing a data strategy, tasks are scheduled, operation logs are recorded, transparency and traceability of data use are guaranteed, a federal privacy protection computing engine adopts a central coordinator to cooperatively work with a local computing agent, and the data security is improved. A deep learning model based on a time sequence is trained through a federal learning mechanism, and the correction ability and long-term prognosis influence of a specific administration scheme on metabolic acidosis are quantitatively evaluated. According to the method, privacy protection is enhanced through anonymization, pseudonymization and security aggregation technologies, multi-mechanism collaborative evaluation is supported, and data utilization and security compliance are considered.

Provided is an automatic pseudonymization technique recommendation method using artificial intelligence, including receiving a training dataset including a plurality of pieces of data including a column name, a data type, a pseudonymization technique recommendation, adding, a data type vector obtained from the data type to a word vector obtained corresponding to the column name to obtain a numeric vector, and labeling the numeric vector with the pseudonymization technique recommendation to generate a plurality of pieces of training data, training a first learning model using the plurality of pieces of training data so that the first learning model outputs a pseudonymization technique recommendation in response to an input of the numeric vector, obtaining a numeric vector corresponding to each column of the dataset to be pseudonymized, and inputting the numeric vector obtained into the trained first learning model to obtain a pseudonymization technique recommendation for each column of the dataset.

A system for data protection-compliant collaboration with customer data using AI in Salesforce cleanrooms, including: a data acquisition module configured to receive customer data from one or more data sources; a data processing module that is functionally coupled with the data acquisition module and configured to transform, normalize, and standardize the received data; a data protection enforcement module that is functionally coupled with the data processing module and configured to use privacy-friendly techniques, including at least one of the following: anonymization, pseudonymization, tokenization or masking to protect sensitive information; a secure data storage module configured to store privacy-protected data in encrypted form; an AI engine that is functionally coupled with the secure data storage module and configured to perform analysis, pattern recognition and predictive modeling on the privacy-protected data without disclosing the underlying raw data; a collaboration module configured to enable controlled data exchange between multiple entities based on predefined access policies and permissions; a secure computing framework configured to perform joint data analysis across multiple entities without directly exchanging raw data sets; and A monitoring and compliance module configured to track data access, enforce privacy policies, and generate audit logs, with the system configured to enable collaborative analysis of customer data within a secure environment such as Salesforce Clean Rooms, while maintaining data confidentiality, ownership rights, and compliance with legal regulations.

The invention discloses systems and methods for privacy-preserving digital communications compliant with frameworks such as GDPR, DPDPA, HIPAA, and PSD2. A Virtual Identity (VI) is instantiated within a secure enclave and bound to one or more Compliance Jurisdiction Tokens (CJTs). Unlike conventional tokenisation limited to payment or aliasing, the invention enables multiple concurrent VIs (Multi-VI), each scoped to a declared purpose, jurisdiction, subnet, or session. Communication is permitted only if all bound CJTs validate inline, including Multi-CJT bindings where jurisdiction, purpose, and consent must all succeed, and Multi-Purpose CJTs (MCJTs) where multiple lawful purposes must be simultaneously satisfied. Each validation produces a Ledger-Anchored Validation Receipt (LAVR) containing pseudonymised metadata, anchored into tamper-evident ledgers for transparency. Regulators access receipts through a Regulator Query Interface (RQI), enabling filtered oversight without exposure of raw identifiers. The technical effect is to transform privacy and compliance into cryptographic enforcement across device, telecom, and network layers

The invention relates to a method for operating a guidance system for motor vehicles and to a guidance system for motor vehicles. The method comprises the following: detecting an available parking space (6) by means of a detection device (32) of a first motor vehicle (2) while the first motor vehicle is positioned adjacent to the available parking space; transmitting parking space data, indicating the available parking space and a parking space position assigned to the available parking space, from the first motor vehicle (2) to a central computer system; providing the parking space data in the central computer system as anonymized or pseudonymized parking space data, such that the parking space data is anonymized or pseudonymized at least with regard to the first motor vehicle (2) and / or a first user to whom the first motor vehicle (2) is assigned;Transmitting parking information, which includes information about the available parking space (6) and the parking space position assigned to it, generated from the anonymized or pseudonymized parking data, from the central computer system to a user interface and outputting the parking information to a second user of a second motor vehicle.

The present disclosure relates to a liveness identify in a machine-to-machine (M2M) system, and a method for operating an M2M device may include identifying a protection regulation applied to data, identifying a protection mechanism applied to the data, and transmitting a resource creation request message including information indicating the protection regulation and the protection mechanism to a second M2M device, and the data may include personal information or privacy information that is generated by an application executed in the M2M device.

Systems and methods for tokenization to support pseudonymization are provided herein. An example method includes receiving an input set, seeding a random number generator with one or more secret data, transposing the input set using a first random number / transposition parameter generated by the random number generator to create a transposed input set, transposing a token set using a second random number / transposition parameter generated by the random number generator to create a transposed token set, and generating a token by substituting transposed input set values with transposed token set values.

Systems and methods for tokenization to support pseudonymization are provided herein. An example method includes receiving an input set, seeding a random number generator with one or more secret data, transposing the input set using a first random number / transposition parameter generated by the random number generator to create a transposed input set, transposing a token set using a second random number / transposition parameter generated by the random number generator to create a transposed token set, and generating a token by substituting transposed input set values with transposed token set values.

An automated data broker system is provided. The automated data broker system serves as an intermediary system between input user data including user interactions with various types of services (for example, a video streaming platform, online retail shopping platform, video game streaming platform, etc.). The automated data broker system receives the various input data and modifies, filters, or otherwise transforms the input data using one or more APIs to produce output data that may then be provided to downstream systems for use and storage. The output data may be standardized in a form that is suitable for use in accordance with various rules relating to user data. For example, the output data may be pseudonymized and input data associated with users who have “opted-out” of data collection may be filtered out.