The Price of Unlearning: Identifying Unlearning Risk in Edge Computing

Abstract

Machine unlearning is an emerging paradigm that aims to make machine learning models “forget” what they have learned about particular data. It fulfills the requirements of privacy legislation (e.g., GDPR), which stipulates that individuals have the autonomy to determine the usage of their personal data. However, alongside all the achievements, there are still loopholes in machine unlearning that may cause significant losses for the system, especially in edge computing. Edge computing is a distributed computing paradigm with the purpose of migrating data processing tasks closer to terminal devices. While various machine unlearning approaches have been proposed to erase the influence of data sample(s), we claim that it might be dangerous to directly apply them in the realm of edge computing. A malicious edge node may broadcast (possibly fake) unlearning requests to a target data sample (s) and then analyze the behavior of edge devices to infer useful information. In this paper, we exploited the vulnerabilities of current machine unlearning strategies in edge computing and proposed a new inference attack to highlight the potential privacy risk. Furthermore, we developed a defense method against this particular type of attack and proposed the price of unlearning ( PoU ) as a means to evaluate the inefficiency it brings to an edge computing system. We provide theoretical analyses to show the upper bound of the PoU using tools borrowed from game theory. The experimental results on real-world datasets demonstrate that the proposed defense strategy is effective and capable of preventing an adversary from deducing useful information.