SecFed: A Secure and Efficient Federated Learning Based on Multi-Key Homomorphic Encryption

Federated Learning (FL) is widely used in various industries because it effectively addresses the predicament of isolated data island. However, eavesdroppers is capable of inferring user privacy from the gradients or models transmitted in FL. Homomorphic Encryption (HE) can be applied in FL to prote...

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Bibliographic Details
Published inIEEE transactions on dependable and secure computing Vol. 21; no. 4; pp. 3817 - 3833
Main Authors Cai, Yuxuan, Ding, Wenxiu, Xiao, Yuxuan, Yan, Zheng, Liu, Ximeng, Wan, Zhiguo
Format Journal Article
LanguageEnglish
Published Washington IEEE 01.07.2024
IEEE Computer Society
Subjects
Algorithms
Computational modeling
Cryptography
Data models
Data privacy
Efficiency
Encryption
Federated learning
homomorphic encryption
Privacy
privacy preservation
Security
Servers
Training
trusted execution environment
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Summary:Federated Learning (FL) is widely used in various industries because it effectively addresses the predicament of isolated data island. However, eavesdroppers is capable of inferring user privacy from the gradients or models transmitted in FL. Homomorphic Encryption (HE) can be applied in FL to protect sensitive data owing to its computability over ciphertexts. However, traditional HE as a single-key system cannot prevent dishonest users from intercepting and decrypting the ciphertexts from cooperative users in FL. Guaranteeing privacy and efficiency in this multi-user scenario is still a challenging target. In this article, we propose a secure and efficient Federated Learning scheme (SecFed) based on multi-key HE to preserve user privacy and delegate some operations to TEE to improve efficiency while ensuring security. Specifically, we design the first TEE-based multi-key HE cryptosystem (EMK-BFV) to support privacy-preserving FL and optimize operation efficiency. Furthermore, we provide an offline protection mechanism to ensure the normal operation of system with disconnected participants. Finally, we give their security proofs and show their efficiency and superiority through comprehensive simulations and comparisons with existing schemes. SecFed offers a 3x performance improvement over TEE-based scheme and a 2x performance improvement over HE-based solution.
ISSN:1545-5971
1941-0018
DOI:10.1109/TDSC.2023.3336977