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:本文测量了稀疏性如何使神经网络更具有抗拒成员推断攻击的能力。获得的经验结果显示,稀疏性可以提高网络的隐私性,同时在所处理的任务上保持相当的性能。该经验研究补充并扩展了现有文献。