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      PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension

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          Abstract

          Background

          Advances in DNA sequencing technologies have prompted a wide range of genomic applications to improve healthcare and facilitate biomedical research. However, privacy and security concerns have emerged as a challenge for utilizing cloud computing to handle sensitive genomic data.

          Methods

          We present one of the first implementations of Software Guard Extension (SGX) based securely outsourced genetic testing framework, which leverages multiple cryptographic protocols and minimal perfect hash scheme to enable efficient and secure data storage and computation outsourcing.

          Results

          We compared the performance of the proposed PRESAGE framework with the state-of-the-art homomorphic encryption scheme, as well as the plaintext implementation. The experimental results demonstrated significant performance over the homomorphic encryption methods and a small computational overhead in comparison to plaintext implementation.

          Conclusions

          The proposed PRESAGE provides an alternative solution for secure and efficient genomic data outsourcing in an untrusted cloud by using a hybrid framework that combines secure hardware and multiple crypto protocols.

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          Most cited references44

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          Identifying personal genomes by surname inference.

          Sharing sequencing data sets without identifiers has become a common practice in genomics. Here, we report that surnames can be recovered from personal genomes by profiling short tandem repeats on the Y chromosome (Y-STRs) and querying recreational genetic genealogy databases. We show that a combination of a surname with other types of metadata, such as age and state, can be used to triangulate the identity of the target. A key feature of this technique is that it entirely relies on free, publicly accessible Internet resources. We quantitatively analyze the probability of identification for U.S. males. We further demonstrate the feasibility of this technique by tracing back with high probability the identities of multiple participants in public sequencing projects.
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            The Elliptic Curve Digital Signature Algorithm (ECDSA)

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              Routes for breaching and protecting genetic privacy.

              We are entering an era of ubiquitous genetic information for research, clinical care and personal curiosity. Sharing these data sets is vital for progress in biomedical research. However, a growing concern is the ability to protect the genetic privacy of the data originators. Here, we present an overview of genetic privacy breaching strategies. We outline the principles of each technique, indicate the underlying assumptions, and assess their technological complexity and maturation. We then review potential mitigation methods for privacy-preserving dissemination of sensitive data and highlight different cases that are relevant to genetic applications.
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                Author and article information

                Contributors
                f4chen@ucsd.edu
                cwang132@syr.edu
                wed004@ucsd.edu
                x1jiang@ucsd.edu
                noman@cs.umanitoba.ca
                azizmma@cs.umanitoba.ca
                sadat@cs.umanitoba.ca
                cenksahi@indiana.edu
                klauter@microsoft.com
                shw070@ucsd.edu
                Conference
                BMC Med Genomics
                BMC Med Genomics
                BMC Medical Genomics
                BioMed Central (London )
                1755-8794
                26 July 2017
                26 July 2017
                2017
                : 10
                Issue : Suppl 2 Issue sponsor : Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
                : 48
                Affiliations
                [1 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Department of Biomedical Informatics, , University of California San Diego, ; La Jolla, 92093 CA USA
                [2 ]ISNI 0000 0001 2189 1568, GRID grid.264484.8, Department of Computer Science, , Syracuse University, ; Syracuse, 13244 NY USA
                [3 ]ISNI 0000 0004 1936 9609, GRID grid.21613.37, Department of Computer Science, , University of Manitoba, ; Winnipeg, R3T 2N2 MB Canada
                [4 ]ISNI 0000 0001 0790 959X, GRID grid.411377.7, Department of Computer Science and Informatics, , Indiana University, ; Bloomington, 47408 IN USA
                [5 ]ISNI 0000 0001 2181 3404, GRID grid.419815.0, , Cryptography Group, Microsoft Research, ; San Diego,, 92122 CA USA
                Article
                281
                10.1186/s12920-017-0281-2
                5547453
                f879b6cc-16b0-4fe7-b203-c3c8333c650e
                © 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.

                iDASH Privacy and Security Workshop 2016
                iDASH 2016
                Chicago, IL, USA
                11/11/2016
                History
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Genetics
                sgx,homomorphic encryption,data outsourcing,privacy preserving
                Genetics
                sgx, homomorphic encryption, data outsourcing, privacy preserving

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