A privacy preserving federated learning scheme using homomorphic encryption and secret sharing

Zhaosen Shi (), Zeyu Yang (), Alzubair Hassan (), Fagen Li () and Xuyang Ding ()
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Zhaosen Shi: University of Electronic Science and Technology of China
Zeyu Yang: University of Electronic Science and Technology of China
Alzubair Hassan: University College Dublin
Fagen Li: University of Electronic Science and Technology of China
Xuyang Ding: University of Electronic Science and Technology of China

Telecommunication Systems: Modelling, Analysis, Design and Management, 2023, vol. 82, issue 3, No 5, 419-433

Abstract: Abstract The performance of machine learning models largely depends on the amount of data. However, with the improvement of privacy awareness, data sharing has become more and more difficult. Federated learning provides a solution for joint machine learning, which alleviates this difficulty. Although it works by sharing parameters instead of data, privacy threats like inference attacks still exist owing to the exposed parameters or updates. In this paper, we propose a privacy preserving scheme for federated learning by combining the homomorphism of both secret sharing and encryption. Our scheme ensures the confidentiality of local parameters and tolerates collusion threats under a certain range. Our scheme also tolerates dropping of some clients, performs aggregation without sharing keys and has simple interaction process. Meantime, we use the automatic protocol tool ProVerif to verify its cryptographic functionality, analyze its theoretical complexity and compare them with similar schemes. We verify our scheme by experiment to show that it has less running time compared with some schemes.

Keywords: Federated learning; Privacy preserving; Homomorphism; Secret sharing (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11235-022-00982-3

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