Provable Privacy Attacks on Trained Shallow Neural Networks

Guy Smorodinsky, Gal Vardi, Itay Safran

Keywords: Deep Learning Theory, Privacy, Neural Networks, Membership Inference Attack

Abstract: We study what provable privacy attacks can be shown on trained, 2-layer ReLU neural networks. We explore two types of attacks; data reconstruction attacks, and membership inference attacks. We prove that theoretical results on the implicit bias of 2-layer neural networks can be used to provably reconstruct a set of which at least a constant fraction are training points in a univariate setting, and can also be used to identify with high probability whether a given point was used in the training set in a high dimensional setting. To the best of our knowledge, our work is the first to show provable vulnerabilities in this setting.

Primary Area: learning theory

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Submission Number: 4437