Cryptology ePrint Archive: Report 2019/294

Semi-parallel Logistic Regression for GWAS on Encrypted Data

Miran Kim and Yongsoo Song and Baiyu Li and Daniele Micciancio

Abstract: The sharing of biomedical data is crucial to enable scientific discoveries across institutions and improve health care. For example, genome-wide association studies (GWAS) based on a large number of samples can identify disease-causing genetic variants. The privacy concern, however, has become a major hurdle for data management and utilization. Homomorphic encryption is one of the most powerful cryptographic primitives which can address the privacy and security issues. It supports the computation on encrypted data so that we can aggregate data and perform an arbitrary computation on an untrusted cloud environment without the leakage of sensitive information.

This paper presents a secure outsourcing solution to assess logistic regression models for quantitative traits to test their associations with genotypes. We adapt the semi-parallel training method by Sikorska et al., which builds a logistic regression model for covariates, followed by one-step parallelizable regressions on all individual single nucleotide polymorphisms (SNPs). In addition, we modify our underlying approximate homomorphic encryption scheme for performance improvement.

We evaluate the performance of our solution through experiments on real-world dataset. It achieves the best performance of homomorphic encryption system for GWAS analysis in terms of both complexity and accuracy. For example, given a dataset consisting of 245 samples, each of which has 10643 SNPs and 3 covariates, our algorithm takes about 41 seconds to perform logistic regression based genome wide association analysis over encryption. We demonstrate the feasibility and scalability of our solution.

Category / Keywords: applications / Homomorphic encryption and Genome-wide association studies and Logistic regression

Date: received 13 Mar 2019

Contact author: miran kim at uth tmc edu

Available format(s): PDF | BibTeX Citation

Version: 20190320:102301 (All versions of this report)

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