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

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
$\sim 43.36\times$
in running time.