SLeak: 分割学習に対するマルチターゲットプライバシースティーリング攻撃

Split Learning (SL) is a distributed learning framework that has gained popularity for its privacy-preserving nature and low computational demands. However, recent studies have the potential that a server adversary to carry out inference attacks, compromising the privacy of victim clients. Nevertheless, upon re-evaluating prior studies, we found that existing methods rely on overly strong assumptions to enhance their performance, resulting in a significant decline in effectiveness under more realistic scenarios. In this work, we provide new insights into the inherent vulnerabilities of SL. Specifically, we discover that both the smashed data and the server model contain the client's representation preference, which the server adversary can exploit to build a substitute client that approximates the target client's unique feature extraction behavior. With a well-trained substitute client, the server can perfectly steal the target client's functionality, training data, and labels. Building on this observation, we introduce Split Leakage (SLeak), a new threat that targets multiple privacy stealing objectives against SL. Notably, SLeak does not depend on strong privacy priors and only requires partial same-domain auxiliary public data to conduct the attacks. Experimental results on diverse datasets and target models show that SLeak surpasses the state-of-the-art method across multiple metrics. Moreover, ablation studies further confirm its robustness and applicability under various scenarios and assumptions.