PubMed · 41719195
Secure bioinformatics: privacy-preserving federated analytics using homomorphic encryption.
Abstract
MOTIVATION: Large-scale bioinformatics analyses increasingly require collaboration across multiple cohorts and institutions, yet existing workflows often rely on data co-localization, which is slow, difficult to scale, and raises privacy concerns. We present a privacy-preserving federated analytics framework that enables secure statistical analysis across distributed datasets without transferring raw data, by performing all computations on encrypted data via cryptographic methods. RESULTS: We evaluate the framework by validating polygenic risk scores and conducting meta-analyses on two real-world cohorts. The proposed solution achieves over 99.9% accuracy relative to plaintext analyses, while maintaining scalable runtime performance with increasing data size and number of participating sites. These results demonstrate the feasibility of secure federated analytics for practical bioinformatics applications involving sensitive data.
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Weizhuang Zhou, Chao Jin, Zexi Yao, Meenatchi Sundaram Muthu Selva Annamalai, Yu En Chan, Sreejith Kumar Ashish Jith, Xiaoxia Deng, Fook Mun Chan, Kok Leong Foong, Rayden Chua Ming Hong, Kok Wai Wong, Roger Foo Sik Yin, Carolyn S P Lam, Arthur Mark Richards, Weng Khong Lim, Jonathan Yap, Khung Keong Yeo, Boon Ooi Patrick Tan, Neerja Karnani, Pavitra Krishnaswamy, Sebastian Maurer-Stroh, Khin Mi Mi Aung. 2026-05-03. Secure bioinformatics: privacy-preserving federated analytics using homomorphic encryption.. https://doi.org/10.1093/bioinformatics%2Fbtag081
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