Fook Mun Chan
ABSTRACT
Genotype imputation is a technique used to determine unobserved genomic markers when sequencing genomic data. This is a cost effective method for sequencing a genome. Due to the large amount of personal identifiable information involved in genomic imputation, there is a rising concern for analysis of such nature to be secure and private.
We describe a method using homomorphic encryption (HE) to perform genotype imputation in a secure and private setting. Our solution first involves training a logistic regression model and performing the imputation in the encrypted domain.
We have implemented our solution over using the open sourced Homomorphic Encryption library, SEAL. We are able to impute 500 SNPs within 5 minutes, with an accuracy of 97.3%.
- [n.d.]. FAQ for iDASH Privacy Protection competition. https://docs.google.com/document/d/1bVkRyrYQFbhpJdIdBgb4vkOeXpv5h51dkQuqw-y3U-s/edit Last Accessed 28 May 2021.Google Scholar
- [n.d.]. iDASH Privacy & Security Workshop 2019. http://www.humangenomeprivacy.org/2019/competition-tasks.html Last Accessed 28 May 2021.Google Scholar
- 2019. Simple Encrypted Arithmetic Library (release 3.2.0). https://github.com/Microsoft/SEAL. Microsoft Research, Redmond, WA., Last Accessed 18 July 2019, commit e5fee8f.Google Scholar
- Martin R. Albrecht, Rachel Player, and Sam Scott. 2015. On the concrete hardness of Learning with Errors. Cryptology ePrint Archive, Report 2015/046. https://eprint.iacr.org/2015/046.Google Scholar
- Jung Hee Cheon, Andrey Kim, Miran Kim, and Yongsoo Song. 2016. Homomorphic Encryption for Arithmetic of Approximate Numbers. Cryptology ePrint Archive, Report 2016/421. http://eprint.iacr.org/2016/421.Google Scholar
- Craig Gentry. 2009. Fully homomorphic encryption using ideal lattices. In 41st ACM Symposium on Theory of Computing. ACM Press, 169–178.Google ScholarDigital Library
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825–2830.Google ScholarDigital Library
- Jonathan K. Pritchard and Molly Przeworski. 2001. Linkage Disequilibrium in Humans: Models and Data. The American Journal of Human Genetics 69, 1 (2001), 1–14. https://doi.org/10.1086/321275Google ScholarCross Ref
- R L Rivest, L Adleman, and M L Dertouzos. 1978. On Data Banks and Privacy Homomorphisms. Foundations of Secure Computation, Academia Press (1978).Google Scholar
- P. Scheet and M. Stephens. 2006. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet 78, 4 (Apr 2006), 629–644.Google ScholarCross Ref
- M. Slatkin. 2008. Linkage disequilibrium–understanding the evolutionary past and mapping the medical future. Nat Rev Genet 9, 6 (Jun 2008), 477–485.Google ScholarCross Ref
- N.P. Smart and F. Vercauteren. 2011. Fully Homomorphic SIMD Operations. Cryptology ePrint Archive, Report 2011/133. https://eprint.iacr.org/2011/133.Google Scholar
Index Terms
- Genotype Imputation with Homomorphic Encryption
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