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This research establishes the first provable vulnerabilities in 2-layer ReLU neural networks, demonstrating how implicit bias can be exploited for membership inference and data reconstruction attacks. The findings are critical for understanding the theoretical limits of privacy guarantees in shallow models within high-dimensional or univariate settings.
Provable Privacy Attacks on Trained Shallow Neural Networks
arXiv:2410.07632v3 Announce Type: replace Abstract: We study what provable privacy attacks can be shown for trained 2-layer ReLU neural networks, focusing on two types of attacks: membership inference and data reconstruction. We prove that theoretical results on the implicit bias of 2-layer neural networks can be used to provably identify with high probability whether a given point was used in the training set in a high-dimensional, nearly orthogonal setting, and can also be used to construct a finite set of which at least a constant fraction are training points in a univariate setting. To the best of our knowledge, our work is the first to show provable vulnerabilities in this implicit-bias-driven setting.
Abstract:We study what provable privacy attacks can be shown for trained 2-layer ReLU neural networks, focusing on two types of attacks: membership inference and data reconstruction. We prove that theoretical results on the implicit bias of 2-layer neural networks can be used to provably identify with high probability whether a given point was used in the training set in a high-dimensional, nearly orthogonal setting, and can also be used to construct a finite set of which at least a constant fraction are training points in a univariate setting. To the best of our knowledge, our work is the first to show provable vulnerabilities in this implicit-bias-driven setting.
Machine Learning (cs.LG); Cryptography and Security (cs.CR)
arXiv:2410.07632 [cs.LG]
(or arXiv:2410.07632v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2410.07632 Focus to learn more arXiv-issued DOI via DataCite
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