A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data

TL;DR

This paper introduces a hybrid federated learning approach combining homomorphic encryption and differential privacy to enhance privacy and accuracy in omics data analysis, optimizing for diverse client computational capabilities.

Contribution

The proposed PPML-Hybrid method allows clients to choose between HE and DP, balancing privacy and computational efficiency, which improves predictive accuracy and reduces computation time.

Findings

Achieves comparable accuracy to HE-only methods with lower computation time.

Outperforms DP-only methods under similar privacy constraints.

Adapts to heterogeneous client resources for privacy-preserving omics data analysis.

Abstract

Omics data is widely employed in medical research to identify disease mechanisms and contains highly sensitive personal information. Federated Learning (FL) with Differential Privacy (DP) can ensure the protection of omics data privacy against malicious user attacks. However, FL with the DP method faces an inherent trade-off: stronger privacy protection degrades predictive accuracy due to injected noise. On the other hand, Homomorphic Encryption (HE) allows computations on encrypted data and enables aggregation of encrypted gradients without DP-induced noise can increase the predictive accuracy. However, it may increase the computation cost. To improve the predictive accuracy while considering the computational ability of heterogeneous clients, we propose a Privacy-Preserving Machine Learning (PPML)-Hybrid method by introducing HE. In the proposed PPML-Hybrid method, clients distributed select either HE or DP based on their computational resources, so that HE clients contribute noise-free updates while DP clients reduce computational overhead. Meanwhile, clients with high computational resources clients can flexibly adopt HE or DP according to their privacy needs. Performance evaluation on omics datasets show that our proposed method achieves comparable predictive accuracy while significantly reducing computation time relative to HE-only. Additionally, it outperforms DP-only methods under equivalent or stricter privacy budgets.