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Secure multi-party linear regression at plaintext speed

Jonathan M. Bloom, Hail Team / Neale Lab / Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard

We detail distributed algorithms for scalable, secure multi-party linear regression and feature selection at essentially the same speed as plaintext regression. While the core geometric ideas are simple, their utility in combination for feature selection appears to be novel. Our scheme opens the door to efficient and secure genome-wide association studies across multiple biobanks.

Read the arXiv note and watch a video primer presented at Models, Inference & Algorithms at the Broad Institute.

We recently found that the approach to SMC linear regression in Section 2 has been explored since at least 2005, c.f. Secure Regression on Distributed Databases.

Run a Python demo of the multi-party linear regression algorithm in Section 2.

Run a Python demo and R demo of the distributed association scan hammer (DASH) in Section 4.

Hail uses the single-party distributed algorithm in Section 3 to enable massive genomic analyses and will include multi-party algorithms someday. Also check out the exciting work on secure genomics by Hoon Cho, Bonnie Berger and colleagues.

An OpenMined implementation is being supported by an RAAIS OpenMined Grant, awarded to André Farias.

Feedback welcome! Write Jon:


Secure multi-party linear regression at plaintext speed




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