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：本文通过实验衡量了稀疏性如何提升神经网络对成员推断攻击的鲁棒性。实证结果表明，稀疏性在保持网络性能与目标任务相当的前提下，有效改善了网络的隐私保护性能。本实证研究对现有文献进行了补充与拓展。