Privacy Risks of Explaining Machine Learning Models.
Reza Shokri, Martin Strobel, Yair Zick · arXiv (Cornell University) · 2019
Can an adversary exploit model explanations to infer sensitive information about the models' training set? To investigate this question, we first focus on membership inference attacks: given a data point and a model explanation, the attacker's goal is to decide whether or not the point belongs to the training data. We study this problem for two popular transparency methods: gradient-based attribution methods and record-based influence measures. We develop membership inference attacks based on these model explanations, and extensively test them on a variety of datasets. For gradient-based methods, we show that the explanations can leak a significant amount of information about the individual data points in the training set, much beyond what is leaked through the predicted labels. We also show that record-based measures can be effectively, and even more significantly, exploited for membership inference attacks. More importantly, we design reconstruction attacks against this class of model explanations. We demonstrate that they can be exploited to recover significant parts of the training set. Finally, our results indicate that minorities and outliers are more vulnerable to these type of attacks than the rest of the population. Thus, there is a significant disparity for the privacy risks of model explanations across different groups.