Multi-client Functional Encryption for Set Intersection with Non-monotonic Access Structures in Federated Learning

TL;DR

This paper introduces a novel multi-client functional encryption scheme for set intersection with complex access controls, enhancing privacy and flexibility in federated learning environments.

Contribution

It presents the first scheme supporting non-monotonic access structures with independent client encryption and formal security proof, tailored for federated learning.

Findings

Scheme resists

Formal security proof provided

Efficient implementation and analysis

Abstract

Federated learning (FL) based on cloud servers is a distributed machine learning framework that involves an aggregator and multiple clients, which allows multiple clients to collaborate in training a shared model without exchanging data. Considering the confidentiality of training data, several schemes employing functional encryption (FE) have been presented. However, existing schemes cannot express complex access control policies. In this paper, to realize more flexible and fine-grained access control, we propose a multi-client functional encryption scheme for set intersection with non-monotonic access structures (MCFE-SI-NAS), where multiple clients co-exist and encrypt independently without interaction. All ciphertexts are associated with an label, which can resist "mix-and-match" attacks. Aggregator can aggregate ciphertexts, but cannot know anything about the plaintexts. We first formalize the definition and security model for the MCFE-SI-NAS scheme and build a concrete construction based on asymmetric prime-order pairings. The security of our scheme is formally proven. Finally, we implement our MCFE-SI-NAS scheme and provide its efficiency analysis.