论文标题
为医学图像差异的私人残留网络基准测试
Benchmarking Differentially Private Residual Networks for Medical Imagery
论文作者
论文摘要
在本文中，我们衡量$ε$ -Differential隐私（DP）的有效性。我们比较了两种可靠的差异隐私机制：Local-DP和DP-SGD，并在分析医学图像记录时基准其性能。我们分析了模型的准确性与保证的隐私水平之间的权衡，并仔细研究了这些理论隐私保证实际上被证明在现实世界医学环境中的用处。
In this paper we measure the effectiveness of $ε$-Differential Privacy (DP) when applied to medical imaging. We compare two robust differential privacy mechanisms: Local-DP and DP-SGD and benchmark their performance when analyzing medical imagery records. We analyze the trade-off between the model's accuracy and the level of privacy it guarantees, and also take a closer look to evaluate how useful these theoretical privacy guarantees actually prove to be in the real world medical setting.
