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Bibliographic Details
Main Authors:	Gonon, Antoine, Zheng, Léon, Lalanne, Clément, Le, Quoc-Tung, Lauga, Guillaume, Pouliquen, Can
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence Cryptography and Security
Online Access:	https://arxiv.org/abs/2304.10553
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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.