Decentralized Multi-Client Functional Encryption for Inner Product With Applications to Federated Learning

• Published in: IEEE transactions on dependable and secure computing 2024-11, Vol.21 (6), p.5781-5796
• Main Authors: Qian, Xinyuan, Li, Hongwei, Hao, Meng, Xu, Guowen, Wang, Haoyong, Fang, Yuguang
• Format: Article
• Language: English
• Subjects: Cryptography; Decentralized functional encryption; Digital signatures; Encryption; Federated learning; inner product; Machine learning; Privacy; Scalability; Servers; Statistical analysis; Task analysis; Trusted third parties; Vectors
• ISSN: 1545-5971; 1941-0018
• DOI: 10.1109/TDSC.2024.3386357

Decentralized multi-client functional encryption for inner product (DMCFE-IP) enables efficient joint functional computation of private inputs in a secure manner without a trusted third party, which has found successful applications, including distributed statistical analysis and machine learning. However, existing DMCFE-IP schemes suffer several drawbacks, such as lack of support for client dropout, requiring cross-client communication for key generation, and poor efficiency and scalability. To address these issues, we propose an efficient and scalable DMCFE-IP, which supports client dropout and non-interactive decentralized partial decryption key generation. Our scheme mainly exploits appropriate underlying cryptographic primitives, including multi-client functional encryption, digital signature, key agreement, secret sharing, and symmetric encryption, with careful integration to achieve the aforementioned two functionalities. We then extend this scheme to enable privacy-preserving federated learning (PPFL) for the cross-silo scenrio. We provide formal security proof for our scheme and evaluate our DMCFE-IP-based PPFL on several real-world datasets. Compared with the state-of-the-art methods, our approach achieves a speedup of 6.12\sim ∼ 43.36× in running time.