基于自动转换搜索的隐私保护协作学习
Privacy-preserving Collaborative Learning with Automatic Transformation Search
摘要：
协作学习由于其数据隐私保护的好处而受到广泛欢迎：参与者可以联合训练深度学习模型，而无需共享他们的训练集。然而，最近的工作发现，对手可以完全恢复敏感的训练样本的共享梯度。这种重建攻击对协作学习构成了严重威胁。因此，迫切需要有效的缓解解决方案。在本文中，我们建议利用数据增强来击败重建攻击：通过精心选择的变换策略对敏感图像进行预处理，对手无法从相应的梯度中提取任何有用的信息。我们设计了一种新的搜索方法来自动发现合格的政策。我们采用两个新的指标来量化转换对数据隐私和模型可用性的影响，这可以显着加快搜索速度。综合评估表明，该方法发现的策略可以击败现有的重建攻击的协作学习，效率高，对模型性能的影响可以忽略不计。
英文摘要：
Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their training sets. However, recent works discovered that an adversary can fully recover the sensitive training samples from the shared gradients. Such reconstruction attacks pose severe threats to collaborative learning. Hence, effective mitigation solutions are urgently desired. In this paper, we propose to leverage data augmentation to defeat reconstruction attacks: by preprocessing sensitive images with carefully-selected transformation policies, it becomes infeasible for the adversary to extract any useful information from the corresponding gradients. We design a novel search method to automatically discover qualified policies. We adopt two new metrics to quantify the impacts of transformations on data privacy and model usability, which can significantly accelerate the search speed. Comprehensive evaluations demonstrate that the policies discovered by our method can defeat existing reconstruction attacks in collaborative learning, with high efficiency and negligible impact on the model performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
