论文标题
深度转移学习中信息泄漏的全面分析
A Comprehensive Analysis of Information Leakage in Deep Transfer Learning
论文作者
论文摘要
转移学习广泛用于将知识从源域转移到稀缺标记数据的目标域。最近，深度转移学习在各种应用中取得了显着进步。但是，在许多实际情况下，来源和目标数据集通常属于两个不同的组织，在深层转移学习中的潜在隐私问题。在这项研究中，为了彻底分析深度转移学习中的潜在隐私泄漏，我们首先将以前的方法分为三类。基于此，我们展示了导致每个类别中无意间隐私泄漏的特定威胁。此外，我们还提供一些解决方案来防止这些威胁。据我们所知，我们的研究是第一个对深度转移学习方法中信息泄漏问题进行彻底分析并为该问题提供潜在解决方案的研究。在两个公共数据集和一个行业数据集上进行了广泛的实验，以显示不同深层转移学习设置和防御解决方案效果下的隐私泄漏。
Transfer learning is widely used for transferring knowledge from a source domain to the target domain where the labeled data is scarce. Recently, deep transfer learning has achieved remarkable progress in various applications. However, the source and target datasets usually belong to two different organizations in many real-world scenarios, potential privacy issues in deep transfer learning are posed. In this study, to thoroughly analyze the potential privacy leakage in deep transfer learning, we first divide previous methods into three categories. Based on that, we demonstrate specific threats that lead to unintentional privacy leakage in each category. Additionally, we also provide some solutions to prevent these threats. To the best of our knowledge, our study is the first to provide a thorough analysis of the information leakage issues in deep transfer learning methods and provide potential solutions to the issue. Extensive experiments on two public datasets and an industry dataset are conducted to show the privacy leakage under different deep transfer learning settings and defense solution effectiveness.
