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arXiv · 2304.10553

Sparsity in neural networks can improve their privacy

Antoine Gonon·Léon Zheng·Clément Lalanne·Quoc-Tung Le·Guillaume Lauga·Can Pouliquen
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Abstract

This article measures how sparsity can make neural networks more robust to membership inference attacks. The obtained empirical results show that sparsity improves the privacy of the network, while preserving comparable performances on the task at hand. This empirical study completes and extends existing literature.

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Antoine Gonon, Léon Zheng, Clément Lalanne, Quoc-Tung Le, Guillaume Lauga, Can Pouliquen. 2023-04-20. Sparsity in neural networks can improve their privacy. https://arxiv.org/abs/2304.10553

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