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Arxiv
1d
71
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A hierarchical approach for assessing the vulnerability of tree-based classification models to membership inference attack
- Machine learning models can unintentionally reveal confidential data, making them susceptible to membership inference attacks (MIA).
- New methods were introduced to assess the vulnerability of tree-based models efficiently against MIA: analyzing hyperparameter choices before training and examining the model structure after training.
- These new approaches do not guarantee model safety but help in reducing the number of models needing extensive MIA evaluation through a hierarchical filtering process.
- Consistent disclosure risk rankings for hyperparameter combinations across datasets allow the identification of high-risk models before training.
- Analyze hyperparameters to avoid risky configurations during model training.
- Simple human-interpretable rules can be developed to identify potentially high-risk models before training.
- Structural metrics can serve as indicators of MIA vulnerability after model training.
- Hyperparameter-based risk prediction rules show high accuracy in predicting vulnerable combinations without requiring model training.
- Model accuracy does not necessarily correspond to privacy risk, indicating room for optimizing models for performance and privacy simultaneously.
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