PMID: 29854245Jun 2, 2018Paper

SCOTCH: Secure Counting Of encrypTed genomiC data using a Hybrid approach

AMIA ... Annual Symposium Proceedings
Wang ChenghongShuang Wang


As genomic data are usually at large scale and highly sensitive, it is essential to enable both efficient and secure analysis, by which the data owner can securely delegate both computation and storage on untrusted public cloud. Counting query of genotypes is a basic function for many downstream applications in biomedical research (e.g., computing allele frequency, calculating chi-squared statistics, etc.). Previous solutions show promise on secure counting of outsourced data but the efficiency is still a big limitation for real world applications. In this paper, we propose a novel hybrid solution to combine a rigorous theoretical model (homomorphic encryption) and the latest hardware-based infrastructure (i.e., Software Guard Extensions) to speed up the computation while preserving the privacy of both data owners and data users. Our results demonstrated efficiency by using the real data from the personal genome project.

Related Concepts

Cloud Computing
Theoretical Study
Computer Programs and Programming
Genome, Human
Data Security
Online Mendelian Inheritance In Man
Genetic Privacy
Datasets as Topic

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