Title: Protection against Source Inference Attacks in Federated Learning
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Federated Learning, Source Inference Attack, Shuffle Model, Residue Number System

Abstract: Federated Learning (FL) was initially proposed as a privacy-preserving machine learning paradigm. However, FL has been shown to be susceptible to a series of privacy attacks. Recently, there has been concern about the Source Inference Attack (SIA), where an honest-but-curious central server attempts to identify exactly which client owns a given data point which was used in the training phase. Alarmingly, standard gradient obfuscation techniques with Differential Privacy have been shown to be ineffective against SIAs, at least without severely diminishing the accuracy.

In this work, we propose a defense against SIAs within the widely studied shuffle model of FL, where an honest shuffler acts as an intermediary between the clients and the server. First, we demonstrate that standard naive shuffling alone is insufficient to prevent SIAs. To effectively defend against SIAs, shuffling needs to be applied at a more granular level; we propose a novel combination of parameter-level shuffling with the residue number system (RNS). Our approach provides robust protection against SIAs without affecting the accuracy of the joint model and can be seamlessly integrated into other privacy protection mechanisms.

We conduct experiments on a series of models and datasets, confirming that standard shuffling approaches fail to prevent SIAs and that, in contrast, our proposed method reduce the attack’s accuracy to the level of random guessing.

TL;DR: The paper discusses a defense against Source Inference Attacks using the shuffle model of Federated Learning. It proposes RNS-based parameter-level shuffling that preserves accuracy and reduces attack success to random guessing.