论文标题
分散图像分类的拓扑意识差异隐私
Topology-aware Differential Privacy for Decentralized Image Classification
论文作者
论文摘要
在本文中，我们设计了Top-DP，这是一种新颖的解决方案，以优化分散图像分类系统的差异隐私保护。解决方案的关键见解是利用分散式通信拓扑的独特功能来降低噪声量表并提高模型的可用性。 （1）我们使用这种拓扑感知的降噪策略增强了DP-SGD算法，并整合了时间感知的噪声衰减技术。 （2）我们设计了两个新颖的学习方案（同步和异步），以保护具有不同网络连接和拓扑的系统。我们正式分析并证明我们提出的解决方案的DP要求。实验评估表明，我们的解决方案在可用性和隐私之间实现了比以前的工作更好的权衡。据我们所知，从网络拓扑的角度来看，这是第一个DP优化工作。
In this paper, we design Top-DP, a novel solution to optimize the differential privacy protection of decentralized image classification systems. The key insight of our solution is to leverage the unique features of decentralized communication topologies to reduce the noise scale and improve the model usability. (1) We enhance the DP-SGD algorithm with this topology-aware noise reduction strategy, and integrate the time-aware noise decay technique. (2) We design two novel learning protocols (synchronous and asynchronous) to protect systems with different network connectivities and topologies. We formally analyze and prove the DP requirement of our proposed solutions. Experimental evaluations demonstrate that our solution achieves a better trade-off between usability and privacy than prior works. To the best of our knowledge, this is the first DP optimization work from the perspective of network topologies.
