5 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 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 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.

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.

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.