Proofpoint, Inc. (20240338485). Methods And System For Context-Preserving Sensitive Data Anonymization simplified abstract
Contents
Methods And System For Context-Preserving Sensitive Data Anonymization
Organization Name
Inventor(s)
Karl Felix Joehnk of Singapore (SG)
Romain Loic Choukroun of Singapore (SG)
Methods And System For Context-Preserving Sensitive Data Anonymization - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338485 titled 'Methods And System For Context-Preserving Sensitive Data Anonymization
Simplified Explanation: The patent application describes systems and methods for training transformer models while preserving the privacy of sensitive data by anonymizing and transforming it into context-preserving tensors.
- **Anonymization of Sensitive Data:** The system extracts data from documents and irreversibly transforms it into context-preserving tensors.
- **Training Transformer Model:** The model is trained using these context-preserving tensors instead of the original data.
- **Data Repositories:** The system includes one or more data repositories in a network or cloud infrastructure for storing the data.
Potential Applications: This technology can be applied in industries where privacy of sensitive data is crucial, such as healthcare, finance, and legal sectors. It can also be used in research institutions for training models on sensitive datasets.
Problems Solved: The technology addresses the challenge of training machine learning models on sensitive data without compromising privacy and confidentiality.
Benefits: - Enhanced privacy protection for sensitive data - Improved security measures for training machine learning models - Facilitates compliance with data protection regulations
Commercial Applications: Title: Privacy-Preserving Transformer Model Training Technology for Secure Data Processing This technology can be commercially utilized in industries dealing with sensitive data, such as healthcare, finance, and legal sectors. It can also be marketed to research institutions and organizations requiring secure data processing solutions.
Prior Art: Prior art related to this technology may include research papers, patents, or existing systems focusing on privacy-preserving machine learning model training and data anonymization techniques.
Frequently Updated Research: Researchers are constantly exploring new methods and techniques for enhancing privacy-preserving machine learning model training. Stay updated on recent advancements in this field to leverage the latest innovations.
Questions about Privacy-Preserving Transformer Model Training: 1. How does the system ensure the irreversibility of data transformation for privacy preservation? 2. What are the key differences between context-preserving tensors and the original data in training transformer models?
Original Abstract Submitted
systems and methods for privacy-preserving transformer model training are provided. the system includes one or more data repositories in a computer network or cloud infrastructure having data stored therein. the system anonymizes the data in the one or more documents, and trains a transformer model on the data outside of the network. the data includes sensitive information. anonymizing the data includes extracting the data from the one or more documents and irreversibly transforming the data in the one or more documents into context-preserving tensors. training the transformer model on the data comprises using the context-preserving tensors instead of the data to train the transformer model on the data.
