Virtual Machine Data Anonymization for Privacy Compliance
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Solution Overview
Problem
Current data sharing systems face challenges in interoperability due to different data storage formats and stringent privacy regulations, such as HIPAA and GDPR, which restrict the sharing of personal information, making it difficult to securely process and analyze data across various entities.
Innovation Solution
A data processing system that creates secure data silos and temporary vaults to anonymize and normalize data, allowing for controlled access and sharing while maintaining privacy, by using virtual machines and virtual private clouds to manage data access and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is shared across different entities, then research capabilities and data analysis are improved, but data privacy and security are compromised
Solution Approach 1:
A data processing system acts as an intermediary between data sources and research consumers. The system receives data from multiple data sources, processes it through anonymization and normalization, and provides processed data to research consumers. This intermediary architecture enables research capabilities while protecting data privacy through controlled processing.
Solution Approach 2:
The system extracts personally identifiable information and sensitive data elements from raw data sets through anonymization processes. By removing or masking specific data elements that could identify individuals, the system enables data sharing for research while maintaining privacy protections required by regulations like HIPAA and GDPR.
2Productivity
If data from different entities is shared, then data analysis capabilities are improved, but data format incompatibility prevents effective sharing
Solution Approach 1:
The system implements a universal data processing framework that can handle multiple data formats and sources. Through normalization processes, the system converts diverse data formats into a standardized structure, enabling effective data analysis across different entities while maintaining adaptability to various source formats.
3Adaptability or versatility
If data is anonymized and processed, then data sharing is enabled, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary anonymization and normalization processing on data before it is shared or analyzed. By pre-processing data to remove sensitive elements and standardize formats in advance, the system enables faster subsequent analysis while maintaining privacy protections, reducing the time loss associated with real-time processing.
Data Source
AI summary
A method includes a data processing system creating a virtual machine for use with a data owner system in accordance with a temporary credential protocol between the data processing system and the data owner system. The method continues with by the virtual machine accessing memory of the data owner system regarding a set of data records having common data criteria. The method continues by the virtual machine modifying the set of data records to produce a set of shareable data records. The method continues with the data processing system executing a data analysis function on the set of shareable data records to produce an analytical result. The method continues with the data processing system sending the analytical result to a data consumer computing entity.