EdgeLeakage: Membership Information Leakage in Distributed Edge Intelligence Systems

TL;DR

This paper investigates membership inference leakage in distributed edge intelligence systems, demonstrating how data privacy can be compromised and proposing defense mechanisms to mitigate this security threat.

Contribution

It introduces the problem of membership inference attacks in edge intelligence and evaluates defense strategies to protect data privacy in collaborative model generation.

Findings

Membership inference attacks can successfully identify data membership in edge models.

Proposed defenses significantly reduce data leakage and improve privacy protection.

Experimental results validate the effectiveness of the defense mechanisms.

Abstract

In contemporary edge computing systems, decentralized edge nodes aggregate unprocessed data and facilitate data analytics to uphold low transmission latency and real-time data processing capabilities. Recently, these edge nodes have evolved to facilitate the implementation of distributed machine learning models, utilizing their computational resources to enable intelligent decision-making, thereby giving rise to an emerging domain referred to as edge intelligence. However, within the realm of edge intelligence, susceptibility to numerous security and privacy threats against machine learning models becomes evident. This paper addresses the issue of membership inference leakage in distributed edge intelligence systems. Specifically, our focus is on an autonomous scenario wherein edge nodes collaboratively generate a global model. The utilization of membership inference attacks serves to elucidate the potential data leakage in this particular context. Furthermore, we delve into the examination of several defense mechanisms aimed at mitigating the aforementioned data leakage problem. Experimental results affirm that our approach is effective in detecting data leakage within edge intelligence systems, and the implementation of our defense methods proves instrumental in alleviating this security threat. Consequently, our findings contribute to safeguarding data privacy in the context of edge intelligence systems.