期刊
2015 IEEE SECURITY AND PRIVACY WORKSHOPS (SPW)
卷 -, 期 -, 页码 7-16出版社
IEEE
DOI: 10.1109/SPW.2015.14
关键词
-
资金
- Grants-in-Aid for Scientific Research [24680015, 24106010] Funding Source: KAKEN
Logistic regression is the method of choice in most genome-wide association studies (GWAS). Due to the heavy cost of performing iterative parameter updates when training such a model, existing methods have prohibitive communication and computational complexities that make them unpractical for real-life usage. We propose a new sampling-based secure protocol to compute exact statistics, that requires a constant number of communication rounds and a much lower number of computations. The publicly available implementation of our protocol (and its many optional optimisations adapted to different security scenarios) can, in a matter of hours, perform statistical testing of over 600 SNP variables across thousands of patients while accounting for potential confounding factors in the clinical data.
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