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.

翻译：本文研究了稀疏性如何使神经网络更加抵抗成员推断攻击。经验结果表明，稀疏性可以提高网络隐私性，同时保持与任务有关的可比较性能。这项实证研究完善和扩展了现有文献。