Abstract
Privacy has become a major concern in machine learning. In fact, the federated learning is motivated by the privacy concern as it does not allow to transmit the private data but only intermediate updates. However, federated learning does not always guarantee privacy-preservation as the intermediate updates may also reveal sensitive information. In this paper, we give an explicit information-theoretical analysis of a federated expectation maximization algorithm for Gaussian mixture model and prove that the intermediate updates can cause severe privacy leakage. To address the privacy issue, we propose a fully decentralized privacy-preserving solution, which is able to securely compute the updates in each maximization step. Additionally, we consider two different types of security attacks: the honest-but-curious and eavesdropping adversary models. Numerical validation shows that the proposed approach has superior performance compared to the existing approach in terms of both the accuracy and privacy level.
Original language | English |
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Title of host publication | 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings |
Number of pages | 5 |
Publisher | IEEE Signal Processing Society |
Publication date | 2022 |
Pages | 4263-4267 |
ISBN (Print) | 978-1-6654-0541-6 |
ISBN (Electronic) | 978-1-6654-0540-9 |
DOIs | |
Publication status | Published - 2022 |
Event | 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore Duration: 23 May 2022 → 27 May 2022 |
Conference
Conference | 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 |
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Country/Territory | Singapore |
City | Virtual, Online |
Period | 23/05/2022 → 27/05/2022 |
Sponsor | Chinese and Oriental Languages Information Processing Society (COLPIS), Singapore Exhibition and Convention Bureau, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), The Institute of Electrical and Electronics Engineers Signal Processing Society |
Series | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2022-May |
ISSN | 1520-6149 |
Bibliographical note
Publisher Copyright:© 2022 IEEE
Keywords
- differential privacy
- Federated learning
- information-theoretic
- privacy-accuracy
- secure multiparty computation