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arXiv Machine Learning By Guy Smorodinsky, Gal Vardi, Itay Safran

Provable Privacy Attacks on Trained Shallow Neural Networks

Read the original on arXiv Machine Learning →

The paper investigates provable privacy attacks on trained 2‑layer ReLU neural networks, specifically membership inference and data reconstruction. It demonstrates that the implicit bias of such networks can be leveraged to identify, with high probability, whether a given point was part of the training set in high‑dimensional, nearly orthogonal settings, and to construct a finite set containing a constant fraction of training points in a univariate setting. This work claims to be the first to reveal provable vulnerabilities arising from implicit bias in shallow neural networks.

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