Machine Learning Data Access Control for Privacy-Aware Sharing
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Solution Overview
Problem
Existing data access systems lack flexibility and predictability, often restricting access to valuable data that could benefit others without causing harm, leading to missed opportunities for innovation and growth.
Innovation Solution
Implementing machine learning models trained on historical access records to evaluate data requests and elements based on defined access rules, allowing for tiered evaluation of requests, individual data elements, and aggregated data sets to determine appropriate data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rigid data access control systems are used to ensure data security, then data privacy and security are protected, but data sharing opportunities are lost and innovation progress is hindered
Solution Approach 1:
The patent implements dynamic data access control by training machine learning models on historical access records to automatically evaluate and adjust access decisions. The system transitions from static, rigid access rules to dynamic, adaptive access control that can respond to different contexts and requests while maintaining security standards.
Solution Approach 2:
The system changes the parameters of data access control by using machine learning models to evaluate multiple factors including requestor identity, data sensitivity, and potential benefits. This allows the access control parameters to be adjusted based on specific conditions rather than applying uniform restrictions.
2Adaptability or versatility
If complete data access is allowed to enable innovation and sharing, then data utility and innovation opportunities increase, but data privacy and security risks increase
Solution Approach 1:
The patent replaces traditional mechanical access control systems with machine learning-based intelligent evaluation. The ML models automatically assess privacy risks and security concerns by analyzing historical patterns and contextual factors, substituting manual or rule-based security checks with adaptive intelligent evaluation.
Solution Approach 2:
The machine learning models act as intermediaries between data requests and data access decisions. They evaluate the legitimacy and safety of each request by comparing it against learned patterns from historical data, serving as an intelligent mediator that balances sharing opportunities with privacy protection.
3Measurement precision
If manual data access review processes are used to ensure security, then access control accuracy is maintained, but processing time increases and efficiency decreases
Solution Approach 1:
The system implements self-service automated access control evaluation where machine learning models independently assess data access requests without requiring manual review. The models use historical access records to automatically determine whether requests should be granted, reducing dependency on manual processing while maintaining accuracy.
Solution Approach 2:
The machine learning models are pre-trained on historical access records before deployment, performing preliminary learning of access patterns and security considerations. This preliminary training enables the models to make accurate access decisions rapidly without requiring real-time manual analysis.
Data Source
AI summary
Techniques for controlling data access using machine learning are provided. In one aspect, first, second, and third training data sets are generated from a set of historical access records and a set of historical data records, where the access records correspond to requests for data and comprise information identifying whether the request satisfies one or more data access rules, and the data records correspond to data elements and comprise information identifying whether the data element satisfies the one or more data access rules. One or more machine learning models are trained based on the first, second, and third training data sets to generate an output identifying whether requests for data should be granted.