Active Membership Inference Attack under Local Differential Privacy in Federated Learning

• URL: http://arxiv.org/abs/2302.12685v2
• Date: Mon, 24 Jul 2023 21:11:55 GMT
• Title: Active Membership Inference Attack under Local Differential Privacy in Federated Learning
• Authors: Truc Nguyen, Phung Lai, Khang Tran, NhatHai Phan, My T. Thai
• Abstract summary: Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection. We propose a new active membership inference (AMI) attack carried out by a dishonest server in FL.
• Score: 18.017082794703555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection through a coordinating server. In this paper, we propose a new active membership inference (AMI) attack carried out by a dishonest server in FL. In AMI attacks, the server crafts and embeds malicious parameters into global models to effectively infer whether a target data sample is included in a client's private training data or not. By exploiting the correlation among data features through a non-linear decision boundary, AMI attacks with a certified guarantee of success can achieve severely high success rates under rigorous local differential privacy (LDP) protection; thereby exposing clients' training data to significant privacy risk. Theoretical and experimental results on several benchmark datasets show that adding sufficient privacy-preserving noise to prevent our attack would significantly damage FL's model utility.

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