Skip to content
Secure multi-party linear regression at plaintext speed
Jupyter Notebook R
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
LICENSE Initial commit Jan 28, 2019
README.md Update README.md Sep 12, 2019
dash.ipynb switched inverse for solve in dash Sep 4, 2019
dash.r Update dash.r May 3, 2019
multiparty_linear_regression.ipynb broke smc into three stages Sep 4, 2019

README.md

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 is novel. Our scheme opens the door to efficient and secure genome-wide association studies across multiple biobanks.

Read the arXiv preprint.

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 and colleagues.

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

Feedback welcome! Write Jon: jbloom@broadinstitute.org

You can’t perform that action at this time.