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Poisoning-Assisted Property Inference Attack Against Federated Learning by Zhibo Wang; Yuting Huang; Mengkai Song; Libing Wu; Feng Xue; Kui Ren is a Computer Science article available to read on EtoBox.

What is Poisoning-Assisted Property Inference Attack Against Federated Learning about?

Federated learning (FL) has emerged as an ideal privacy-preserving learning technique which can train a global model in a collaborative way while preserving the private data in the local. However, recent advances have demonstrated that FL is still vulnerable to inference attacks, such as reconstruction attack and membership inference. Among these attacks, the property inference attack, aiming to infer properties of the training data that are irrelevant with the learning objective, has not received too much attention while resulting in severe privacy leakage. Existing property inference attack approaches either cannot achieve satisfactory performance when the global model has converged or under dynamic FL where participants can drop in and drop out freely. In this paper, we propose a novel poisoning-assisted property inference attack (PAPI-attack) against FL. The key insight is that there exists underlying discriminative ability in the periodic model updates, which reflects the change of the data distribution, especially the occurrence of the sensitive property. Thus, a binary attack model can be constructed by a malicious participant for inferring the unintended information. More i

Who reads Poisoning-Assisted Property Inference Attack Against Federated Learning?

Author

Zhibo Wang; Yuting Huang; Mengkai Song; Libing Wu; Feng Xue; Kui Ren

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)