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

• URL: http://arxiv.org/abs/2511.06064v1
• Date: Sat, 08 Nov 2025 16:18:42 GMT
• Title: A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data
• Authors: Yusaku Negoya, Feifei Cui, Zilong Zhang, Miao Pan, Tomoaki Ohtsuki, Aohan Li,
• Abstract summary: Homomorphic Encryption (HE) allows computations on encrypted data and enables aggregation of encrypted gradients without DP-induced noise.<n>We propose a Privacy-Preserving Machine Learning (PPML)-Hybrid method by introducing HE.<n>Our proposed method achieves comparable predictive accuracy while significantly reducing computation time relative to HE-only.
• Score: 19.04813252998036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Privacy-Aware Decoding: Mitigating Privacy Leakage of Large Language Models in Retrieval-Augmented Generation [26.573578326262307]
  Privacy-Aware Decoding (PAD) is a lightweight, inference-time defense that adaptively injects calibrated Gaussian noise into token logits during generation.<n>PAD integrates confidence-based screening to selectively protect high-risk tokens, efficient sensitivity estimation to minimize unnecessary noise, and context-aware noise calibration to balance privacy with generation quality.<n>Our work takes an important step toward mitigating privacy risks in RAG via decoding strategies, paving the way for universal and scalable privacy solutions in sensitive domains.
  arXiv  Detail & Related papers  (2025-08-05T05:22:13Z)
• FedRE: Robust and Effective Federated Learning with Privacy Preference [20.969342596181246]
  Federated Learning (FL) employs gradient aggregation at the server for distributed training to prevent the privacy leakage of raw data.<n>Private information can still be divulged through the analysis of uploaded gradients from clients.<n>Existing methods fail to take practical issues into account by merely perturbing each sample with the same mechanism.
  arXiv  Detail & Related papers  (2025-05-08T01:50:27Z)
• Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning [54.20871516148981]
  We introduce the Communication-Efficient and Privacy-Adaptable Mechanism (CEPAM)<n>CEPAM achieves communication efficiency and privacy protection simultaneously.<n>We theoretically analyze the privacy guarantee of CEPAM and investigate the trade-offs among user privacy and accuracy of CEPAM.
  arXiv  Detail & Related papers  (2025-01-21T11:16:05Z)
• Pseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning [59.29849532966454]
  We propose PseudoProbability Unlearning (PPU), a novel method that enables models to forget data to adhere to privacy-preserving manner. Our method achieves over 20% improvements in forgetting error compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-11-04T21:27:06Z)
• Enhancing Accuracy-Privacy Trade-off in Differentially Private Split Learning [2.2676798389997863]
  Split learning (SL) aims to protect user data privacy by distributing deep models between client-server and keeping private data locally. Recently proposed model inversion attacks can recover the original data from the smashed data. A strategy is to adopt differential privacy (DP), which involves safeguarding the smashed data at the expense of some accuracy loss.
  arXiv  Detail & Related papers  (2023-10-22T22:45:13Z)
• Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing [23.80837224347696]
  This paper proposes a privacy-preserving GPR method based on secret sharing (SS) We derive a new SS-based exponentiation operation through the idea of 'confusion-correction' and construct an SS-based matrix inversion algorithm based on Cholesky decomposition. Empirical results show that our proposed method can achieve reasonable accuracy and efficiency under the premise of preserving data privacy.
  arXiv  Detail & Related papers  (2023-06-26T08:17:51Z)
• Decentralized Stochastic Optimization with Inherent Privacy Protection [103.62463469366557]
  Decentralized optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing. Since involved data, privacy protection has become an increasingly pressing need in the implementation of decentralized optimization algorithms.
  arXiv  Detail & Related papers  (2022-05-08T14:38:23Z)
• DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations [54.960853673256]
  We show that strong data augmentations, such as mixup and random additive noise, nullify poison attacks while enduring only a small accuracy trade-off. A rigorous analysis of DP-InstaHide shows that mixup does indeed have privacy advantages, and that training with k-way mixup provably yields at least k times stronger DP guarantees than a naive DP mechanism.
  arXiv  Detail & Related papers  (2021-03-02T23:07:31Z)
• PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting [3.822543555265593]
  This paper presents PRICURE, a system that combines complementary strengths of secure multi-party computation and differential privacy. PRICURE enables privacy-preserving collaborative prediction among multiple model owners. We evaluate PRICURE on neural networks across four datasets including benchmark medical image classification datasets.
  arXiv  Detail & Related papers  (2021-02-19T05:55:53Z)
• Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization [27.243322019117144]
  Federated learning (FL) enables distributed agents to collaboratively learn a centralized model without sharing their raw data with each other. We propose a new FL framework with sparsification-amplified privacy. Our approach integrates random sparsification with gradient perturbation on each agent to amplify privacy guarantee.
  arXiv  Detail & Related papers  (2020-08-01T20:22:57Z)
• RDP-GAN: A R\'enyi-Differential Privacy based Generative Adversarial Network [75.81653258081435]
  Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection. However, when GANs are applied on sensitive or private training examples, such as medical or financial records, it is still probable to divulge individuals' sensitive and private information. We propose a R'enyi-differentially private-GAN (RDP-GAN), which achieves differential privacy (DP) in a GAN by carefully adding random noises on the value of the loss function during training.
  arXiv  Detail & Related papers  (2020-07-04T09:51:02Z)
• Differentially Private Federated Learning with Laplacian Smoothing [72.85272874099644]
  Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. An adversary may still be able to infer the private training data by attacking the released model. Differential privacy provides a statistical protection against such attacks at the price of significantly degrading the accuracy or utility of the trained models.
  arXiv  Detail & Related papers  (2020-05-01T04:28:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.