ML Feature Privacy Risk Assessment Using Differential Inference
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Solution Overview
Problem
Existing methods for assessing privacy risk in machine learning models are inadequate as they fail to provide deep insights into security threats and do not effectively account for various factors, leaving training data vulnerable to advanced attacks.
Innovation Solution
A method involving differential inference attacks is used to identify target features, assess privacy risk scores, and apply mitigation strategies such as anonymization or removal of high-risk features to enhance model privacy while maintaining performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing privacy assessment methods are used, then the assessment process is simple, but the insight into security threats is insufficient
Solution Approach 1:
The patent segments the privacy risk assessment into multiple independent attack vectors (model inversion, membership inference, property inference, data poisoning) and evaluates each separately. This allows comprehensive coverage of different threat types while maintaining clear, modular assessment procedures for each attack vector.
Solution Approach 2:
The patent performs preliminary actions by conducting multiple types of inference attacks and feature tests before finalizing the privacy risk score. This includes model inversion attacks, membership inference attacks, property inference attacks, and feature-level tests, all executed beforehand to gather comprehensive data for the final assessment.
2Reliability
If comprehensive privacy assessment is performed, then security threat insight is improved, but the assessment effectiveness may be reduced
Solution Approach 1:
The patent implements feedback mechanisms by using the results of differential inference attacks to adjust and refine the privacy risk score. The system continuously evaluates the impact of removing target features on model performance and privacy protection, using this feedback to determine optimal feature sets and mitigation strategies.
Solution Approach 2:
The patent changes parameters by adjusting the privacy risk score based on multiple factors including attack results, feature importance, and model performance metrics. The system dynamically modifies assessment parameters to balance comprehensive evaluation with practical efficiency, allowing flexible adjustment of assessment depth based on specific needs.
3Object-affected harmful factors
If target features are removed to enhance privacy, then privacy protection is improved, but model performance may deteriorate
Solution Approach 1:
The patent extracts and removes only the specific target features that contribute most to privacy risks, rather than removing all sensitive features. By identifying and extracting only the high-risk features through differential inference attacks, the system minimizes the impact on model performance while achieving effective privacy protection.
Solution Approach 2:
The patent applies local quality by treating different features differently based on their specific privacy risk levels. High-risk features are removed or protected, while low-risk features are retained to maintain model performance. This selective approach ensures that privacy protection is applied where most needed without unnecessarily degrading overall model functionality.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for assessing privacy risk is provided. The embodiment may include identifying a data set and a machine learning model. The embodiment may also include selecting a target feature set comprising one or more target features of the data set. The embodiment may further include conducting one or more differential inference attacks on the machine learning model based on the target feature set. The embodiment may also include determining a privacy risk score for the target feature set based on results of the one or more differential inference attacks.