Unleashing the Tiger: Inference Attacks on Split Learning

Understand

We investigate the security of Split Learning -- a novel collaborative machine learning framework that enables peak performance by requiring minimal resources consumption.

• In the present paper, we expose vulnerabilities of the protocol and demonstrate its inherent insecurity by introducing general attack strategies targeting the reconstruction of clients' private training sets.
• More prominently, we show that a malicious server can actively hijack the learning process of the distributed model and bring it into an insecure state that enables inference attacks on clients' data.
• We implement different adaptations of the attack and test them on various datasets as well as within realistic threat scenarios.