Privacy Risks of Explaining Machine Learning Models
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Can we trust black-box machine learning with its decisions? Can we trust algorithms to train machine learning models on sensitive data? Transparency and privacy are two fundamental elements of trust for adopting machine learning. In this paper, we investigate the relation between interpretability and privacy. In particular we analyze if an adversary can exploit transparent machine learning to infer sensitive information about its training set. To this end, we perform membership inference as well as reconstruction attacks on two popular classes of algorithms for explaining machine learning models: feature-based and record-based influence measures. We empirically show that an attacker, that only observes the feature-based explanations, has the same power as the state of the art membership inference attacks on model predictions. We also demonstrate that record-based explanations can be effectively exploited to reconstruct significant parts of the training set. Finally, our results indicate that minorities and special cases are more vulnerable to these type of attacks than majority groups.
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