Secure multi-party linear regression at plaintext speed

2019/01/28 by Jonathan M. Bloom, Bloom, Jonathan M. · 1 citation
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Gene expression and cancer classification #Genetic Associations and Epidemiology #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1901.09531

openalex publication_date 2019/01/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We detail distributed algorithms for scalable, secure multiparty linear regression and feature selection at essentially the same speed as plaintext regression. While the core geometric ideas are simple, the recognition of their broad utility when combined is novel. Our scheme opens the door to efficient and secure genome-wide association studies across multiple biobanks.

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• Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent

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