Zero-Trust Data Obfuscation in SGX Enclaves for Private Algorithm Validation
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Solution Overview
Problem
The challenge lies in securely processing sensitive data with proprietary algorithms without exposing either the data or the algorithms to unauthorized parties, particularly in scenarios where data sharing is restricted by size, regulatory compliance, and privacy concerns, as seen in healthcare and AI development.
Innovation Solution
Implementing a zero-trust computing system that encrypts algorithms and data within sequestered computing nodes, using public-private key techniques and Intel SGX enclaves to ensure only authorized entities can decrypt and process the data, maintaining secrecy and compliance with regulations like HIPAA.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is shared with algorithm developers for processing, then algorithm training and validation can be performed, but data privacy and security are compromised
Solution Approach 1:
A trusted third-party platform is introduced as an intermediary between data stewards and algorithm developers. The platform enables secure data sharing through controlled access mechanisms, allowing algorithm validation while maintaining data privacy through encryption and access control policies.
Solution Approach 2:
The system segments data access permissions by creating different levels of access control. Sensitive fields are identified and protected separately from non-sensitive fields, allowing algorithm developers to access only necessary data portions while maintaining security for sensitive information.
2Reliability
If large datasets are transferred to algorithm developers, then comprehensive algorithm training is enabled, but transfer time and bandwidth consumption increase significantly
Solution Approach 1:
The system extracts and identifies only the essential and non-sensitive data fields required for algorithm validation, separating them from the complete dataset. This extraction process eliminates the need to transfer entire large datasets, reducing transfer time while maintaining training effectiveness.
Solution Approach 2:
Instead of transferring complete datasets, the system implements partial data sharing by providing only the subset of data fields necessary for validation. This partial action approach achieves sufficient algorithm validation without the overhead of complete data transfer.
3Object-affected harmful factors
If sensitive data fields are protected and not shared, then data security is maintained, but algorithm validation accuracy may be compromised
Solution Approach 1:
The system applies different security and access characteristics to different data fields based on their sensitivity. Non-sensitive fields are made accessible for validation, while sensitive fields maintain protection. This local differentiation allows algorithm validation to proceed with available fields while maintaining security where required.
4Reliability
If proprietary algorithms are shared with data stewards, then algorithm transparency and validation are improved, but intellectual property protection is weakened
Solution Approach 1:
The trusted platform acts as an intermediary that enables algorithm execution and validation without requiring direct sharing of proprietary algorithm code. The platform provides a secure environment where algorithms can be run and validated while remaining protected from direct exposure to data stewards.
Data Source
AI summary
Systems and methods for data obfuscation are provided. Data obfuscation is needed when protecting an algorithm from reverse engineering attempts. The data is obfuscated by requesting more data from the data steward than is needed by the algorithm. If there are not enough types of data available from the data steward, “low intensity” data types can be requested to fill out the data types requested. These ‘low intensity’ data types are ones that are easily obtained or even regularly collected anyway. The algorithms libraries are altered to call for all the data fields available, thereby rendering reverse engineering extremely difficult, if not impossible.