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Private queries on encrypted genomic data

BACKGROUND: One of the tasks in the iDASH Secure Genome Analysis Competition in 2016 was to demonstrate the feasibility of privacy-preserving queries on homomorphically encrypted genomic data. More precisely, given a list of up to 100,000 mutations, the task was to encrypt the data using homomorphic...

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Autores principales: Çetin, Gizem S., Chen, Hao, Laine, Kim, Lauter, Kristin, Rindal, Peter, Xia, Yuhou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5547495/
https://www.ncbi.nlm.nih.gov/pubmed/28786359
http://dx.doi.org/10.1186/s12920-017-0276-z
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author Çetin, Gizem S.
Chen, Hao
Laine, Kim
Lauter, Kristin
Rindal, Peter
Xia, Yuhou
author_facet Çetin, Gizem S.
Chen, Hao
Laine, Kim
Lauter, Kristin
Rindal, Peter
Xia, Yuhou
author_sort Çetin, Gizem S.
collection PubMed
description BACKGROUND: One of the tasks in the iDASH Secure Genome Analysis Competition in 2016 was to demonstrate the feasibility of privacy-preserving queries on homomorphically encrypted genomic data. More precisely, given a list of up to 100,000 mutations, the task was to encrypt the data using homomorphic encryption in a way that allows it to be stored securely in the cloud, and enables the data owner to query the dataset for the presence of specific mutations, without revealing any information about the dataset or the queries to the cloud. METHODS: We devise a novel string matching protocol to enable privacy-preserving queries on homomorphically encrypted data. Our protocol combines state-of-the-art techniques from homomorphic encryption and private set intersection protocols to minimize the computational and communication cost. RESULTS: We implemented our protocol using the homomorphic encryption library SEAL v2.1, and applied it to obtain an efficient solution to the iDASH competition task. For example, using 8 threads, our protocol achieves a running time of only 4 s, and a communication cost of 2 MB, when querying for the presence of 5 mutations from an encrypted dataset of 100,000 mutations. CONCLUSIONS: We demonstrate that homomorphic encryption can be used to enable an efficient privacy-preserving mechanism for querying the presence of particular mutations in realistic size datasets. Beyond its applications to genomics, our protocol can just as well be applied to any kind of data, and is therefore of independent interest to the homomorphic encryption community.
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spelling pubmed-55474952017-08-09 Private queries on encrypted genomic data Çetin, Gizem S. Chen, Hao Laine, Kim Lauter, Kristin Rindal, Peter Xia, Yuhou BMC Med Genomics Research BACKGROUND: One of the tasks in the iDASH Secure Genome Analysis Competition in 2016 was to demonstrate the feasibility of privacy-preserving queries on homomorphically encrypted genomic data. More precisely, given a list of up to 100,000 mutations, the task was to encrypt the data using homomorphic encryption in a way that allows it to be stored securely in the cloud, and enables the data owner to query the dataset for the presence of specific mutations, without revealing any information about the dataset or the queries to the cloud. METHODS: We devise a novel string matching protocol to enable privacy-preserving queries on homomorphically encrypted data. Our protocol combines state-of-the-art techniques from homomorphic encryption and private set intersection protocols to minimize the computational and communication cost. RESULTS: We implemented our protocol using the homomorphic encryption library SEAL v2.1, and applied it to obtain an efficient solution to the iDASH competition task. For example, using 8 threads, our protocol achieves a running time of only 4 s, and a communication cost of 2 MB, when querying for the presence of 5 mutations from an encrypted dataset of 100,000 mutations. CONCLUSIONS: We demonstrate that homomorphic encryption can be used to enable an efficient privacy-preserving mechanism for querying the presence of particular mutations in realistic size datasets. Beyond its applications to genomics, our protocol can just as well be applied to any kind of data, and is therefore of independent interest to the homomorphic encryption community. BioMed Central 2017-07-26 /pmc/articles/PMC5547495/ /pubmed/28786359 http://dx.doi.org/10.1186/s12920-017-0276-z Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Çetin, Gizem S.
Chen, Hao
Laine, Kim
Lauter, Kristin
Rindal, Peter
Xia, Yuhou
Private queries on encrypted genomic data
title Private queries on encrypted genomic data
title_full Private queries on encrypted genomic data
title_fullStr Private queries on encrypted genomic data
title_full_unstemmed Private queries on encrypted genomic data
title_short Private queries on encrypted genomic data
title_sort private queries on encrypted genomic data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5547495/
https://www.ncbi.nlm.nih.gov/pubmed/28786359
http://dx.doi.org/10.1186/s12920-017-0276-z
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