Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning

TL;DR

This paper analyzes the security of MicroSecAgg, a secure aggregation protocol for federated learning, revealing vulnerabilities and proposing countermeasures to enhance privacy protections.

Contribution

It identifies a security flaw in MicroSecAgg, demonstrates an attack exploiting predictable masking, and suggests improvements for more secure federated learning aggregation.

Findings

MicroSecAgg has a security flaw that can be exploited.

Predictable masking values compromise user privacy.

Countermeasures can mitigate these vulnerabilities.

Abstract

Federated learning enables the collaborative learning of a global model on diverse data, preserving data locality and eliminating the need to transfer user data to a central server. However, data privacy remains vulnerable, as attacks can target user training data by exploiting the updates sent by users during each learning iteration. Secure aggregation protocols are designed to mask/encrypt user updates and enable a central server to aggregate the masked information. MicroSecAgg (PoPETS 2024) proposes a single server secure aggregation protocol that aims to mitigate the high communication complexity of the existing approaches by enabling a one-time setup of the secret to be re-used in multiple training iterations. In this paper, we identify a security flaw in the MicroSecAgg that undermines its privacy guarantees. We detail the security flaw and our attack, demonstrating how an adversary can exploit predictable masking values to compromise user privacy. Our findings highlight the critical need for enhanced security measures in secure aggregation protocols, particularly the implementation of dynamic and unpredictable masking strategies. We propose potential countermeasures to mitigate these vulnerabilities and ensure robust privacy protection in the secure aggregation frameworks.