ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Plant Bioinformatics
A Framework for Privacy-Preserving Similarity Search of Massive Multi-Party Genomic Data
Provisionally accepted
- 1Weifang Institute of Technology, Qingzhou, China
- 2Nilai University, Nilai, Malaysia
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Abstract: To address the challenges of data silos across different institutions, privacy concerns, and the multi-party genomic data matching problem in crop breeding, this paper proposes a Fed-LSH framework. This framework is a collaborative framework integrating privacy-enhanced Locality-Sensitive Hashing (LSH) algorithm with Federated Learning. It enables participants can conduct cross-institutional similar genomic association analysis and elite allele identification, And they do not need to share raw genomic data with each other. This framework utilizes distributed hash index construction, outsourced computation, and encrypted similarity search to accomplish this task. Experiments show that Fed-LSH can achieve a hit rate of 60.72%±1.2% when recommending 4 candidates, using a 40×3 hash size on 3072-dimensional data (implemented on standard personal computers). It can select 4 candidates from 10,000 genomic fragments (each 3072-dimensional) in less than 0.5 seconds. These performance metrics indicate that Fed-LSH provides foundational technical support for privacy-preserving collaborative tomato breeding.
Keywords: Privacy protection, Federated learning, genomic selection, Locality-sensitive hashing, plant breeding
Received: 08 Sep 2025; Accepted: 12 Nov 2025.
Copyright: © 2025 Liu, Xu, Gera, Li and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jia Liu, lsegily@126.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.