Research#Genomics#Privacy-Preserving ML#Logistic Regression
High performance logistic regression for privacy-preserving genome analysis
Our highly-cited work on implementing secure logistic regression for analyzing genomic data while maintaining privacy.
Authors: Martine De Cock, Rafael Dowsley, Anderson C. A. Nascimento, Davis Railsback, Jianwei Shen, Ariel Todoki
Published: 2021-01-20
Abstract
We introduce a high-performance implementation of privacy-preserving logistic regression specifically designed for genomic data analysis, ensuring both computational efficiency and data privacy.
Citation
De Cock, M., Dowsley, R., Nascimento, A.C.A., Railsback, D., Shen, J., & Todoki, A. (2021). High performance logistic regression for privacy-preserving genome analysis. BMC Medical Genomics, 14, 1-18.
Paper Preview
Full Paper
Download the complete paper in PDF format