Provable Privacy Attacks on Trained Shallow Neural Networks
<|reference_start|>Provable Privacy Attacks on Trained Shallow Neural Networks: 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.<|reference_end|>
@article{smorodinsky2024provable,
    title={Provable Privacy Attacks on Trained Shallow Neural Networks},
    author={Guy Smorodinsky and Gal Vardi and Itay Safran},
    journal={arXiv preprint arXiv:2410.07632},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07632},
    primaryClass={cs.LG cs.CR}
}