SplitGuard: Detecting and Mitigating Training-Hijacking Attacks in Split Learning

• URL: http://arxiv.org/abs/2108.09052v2
• Date: Mon, 23 Aug 2021 05:28:50 GMT
• Title: SplitGuard: Detecting and Mitigating Training-Hijacking Attacks in Split Learning
• Authors: Ege Erdogan, Alptekin Kupcu, A. Ercument Cicek
• Abstract summary: Split learning involves dividing a neural network between a client and a server so that the client computes the initial set of layers, and the server computes the rest. Such training-hijacking attacks present a significant risk for the data privacy of split learning clients. We propose SplitGuard, a method by which a split learning client can detect whether it is being targeted by a training-hijacking attack or not.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributed deep learning frameworks, such as split learning, have recently been proposed to enable a group of participants to collaboratively train a deep neural network without sharing their raw data. Split learning in particular achieves this goal by dividing a neural network between a client and a server so that the client computes the initial set of layers, and the server computes the rest. However, this method introduces a unique attack vector for a malicious server attempting to steal the client's private data: the server can direct the client model towards learning a task of its choice. With a concrete example already proposed, such training-hijacking attacks present a significant risk for the data privacy of split learning clients. In this paper, we propose SplitGuard, a method by which a split learning client can detect whether it is being targeted by a training-hijacking attack or not. We experimentally evaluate its effectiveness, and discuss in detail various points related to its use. We conclude that SplitGuard can effectively detect training-hijacking attacks while minimizing the amount of information recovered by the adversaries.

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