Skefl: Single-Key Homomorphic Encryption for Secure Federated Learning
Dongfang Zhao

TL;DR
Skefl introduces an efficient single-key homomorphic encryption protocol with secret sharing to enhance security in federated learning without the performance drawbacks of multi-key schemes.
Contribution
The paper proposes Skefl, a novel protocol that combines single-key homomorphic encryption with secret sharing to prevent collusion attacks in federated learning.
Findings
Achieves a balance between security and efficiency in federated learning.
Provides a cryptographic security proof using the simulation framework.
Demonstrates practical performance benefits over multi-key schemes.
Abstract
Homomorphic encryption (HE) is widely adopted in untrusted environments such as federated learning. A notable limitation of conventional single-key HE schemes is the stringent security assumption regarding collusion between the parameter server and participating clients: Adversary clients are assumed not to collude with the server, as otherwise, the parameter could transmit the ciphertext of one client \(C_0\) to another client \(C_1\), who shares the same private key and could recover the local model of \(C_0\). One plausible solution to alleviate this strong assumption is multi-key HE schemes, which, unfortunately, prove impractically slow in production systems. In this work, we propose a new protocol that achieves the balance between security and performance: We extend single-key HE schemes with efficient secret sharing, ensuring that collusion between the parameter server and any…
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Taxonomy
TopicsCryptography and Data Security · Privacy-Preserving Technologies in Data · Cooperative Communication and Network Coding
