Sketching algorithms for genomic data analysis and querying in a secure enclave

Genome-wide association studies (GWAS), especially on rare diseases, may necessitate exchange of sensitive genomic data between multiple institutions. Since genomic data sharing is often infeasible due to privacy concerns, cryptographic methods, such as secure multiparty computation (SMC) protocols,...

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Bibliographic Details
Published in:Nature methods Vol. 17; no. 3; pp. 295 - 301
Main Authors: Kockan, Can, Zhu, Kaiyuan, Dokmai, Natnatee, Karpov, Nikolai, Kulekci, M. Oguzhan, Woodruff, David P., Sahinalp, S. Cenk
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.03.2020
Nature Publishing Group
Subjects:
631/114/2785
631/114/794
Algorithms
Bioinformatics
Biological Microscopy
Biological Techniques
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Cloud computing
Collaboration
Compression
Computational biology
Computational Biology - methods
Computer applications
Cryptography
Data analysis
Data compression
Data retrieval
Data structures
Genetic research
Genetic Variation
Genome, Human
Genome-wide association studies
Genome-Wide Association Study
Genomes
Genomics
Genomics - methods
Genotype
Humans
Identification methods
Life Sciences
Microprocessors
Phenotype
Polymorphism, Single Nucleotide
Privacy
Proteomics
Rare diseases
Software
United States
ISSN:1548-7091, 1548-7105, 1548-7105
Online Access:Get full text
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Summary:Genome-wide association studies (GWAS), especially on rare diseases, may necessitate exchange of sensitive genomic data between multiple institutions. Since genomic data sharing is often infeasible due to privacy concerns, cryptographic methods, such as secure multiparty computation (SMC) protocols, have been developed with the aim of offering privacy-preserving collaborative GWAS. Unfortunately, the computational overhead of these methods remain prohibitive for human-genome-scale data. Here we introduce SkSES ( https://github.com/ndokmai/sgx-genome-variants-search ), a hardware–software hybrid approach for privacy-preserving collaborative GWAS, which improves the running time of the most advanced cryptographic protocols by two orders of magnitude. The SkSES approach is based on trusted execution environments (TEEs) offered by current-generation microprocessors—in particular, Intel’s SGX. To overcome the severe memory limitation of the TEEs, SkSES employs novel ‘sketching’ algorithms that maintain essential statistical information on genomic variants in input VCF files. By additionally incorporating efficient data compression and population stratification reduction methods, SkSES identifies the top k genomic variants in a cohort quickly, accurately and in a privacy-preserving manner. The combination of Intel SGX platform with sketching algorithms enables efficient compaction of genomic data and the execution of secure GWAS in an untrusted cloud environment.
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C.K., N.D., M.O.K. and S.C.S. initially participated in the iDASH-2017 competition. K.Z., N.K. and S.C.S. formulated the problem with limited memory. K.Z., S.C.S. and D.P.W. further formulated the problem to correct for population stratification. C.K., K.Z. and N.D. implemented the proposed solution. C.K., K.Z., N.D., N.K. and S.C.S. co-wrote the manuscript. M.O.K., D.P.W. and S.C.S. supervised the study.
Author contributions
ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/s41592-020-0761-8