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dc.contributor.authorZhang, Y
dc.contributor.authorZhong, M
dc.contributor.authorZhao, X
dc.contributor.authorCurtis, C
dc.contributor.authorLi, X
dc.contributor.authorChen, C
dc.date.accessioned2021-08-27T03:35:09Z
dc.date.available2021-08-27T03:35:09Z
dc.date.issued2019
dc.identifier.isbn9781450359405
dc.identifier.doi10.1145/3289600.3290983
dc.identifier.urihttp://hdl.handle.net/10072/407345
dc.description.abstractThe human genome can reveal sensitive information and is potentially re-identifiable, which raises privacy and security concerns about sharing such data on wide scales. In this work, we propose a preventive approach for privacy-preserving sharing of genomic data in decentralized networks for Genome-wide association studies (GWASs), which have been widely used in discovering the association between genotypes and phenotypes. The key components of this work are: a decentralized secure network, with a privacy-preserving sharing protocol, and a gene fragmentation framework that is trainable in an end-to-end manner. Our experiments on real datasets show the effectiveness of our privacy-preserving approaches as well as significant improvements in efficiency when compared with recent, related algorithms.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherACM Digital Library
dc.relation.ispartofconferencename12th ACM International Conference on Web Search and Data Mining (WSDM)
dc.relation.ispartofconferencetitleWSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining
dc.relation.ispartofdatefrom2019-02-11
dc.relation.ispartofdateto2019-02-15
dc.relation.ispartoflocationMelbourne, Australia
dc.relation.ispartofpagefrom204
dc.relation.ispartofpageto212
dc.subject.fieldofresearchGenomics
dc.subject.fieldofresearchcode310509
dc.subject.keywordsScience & Technology
dc.subject.keywordsComputer Science, Artificial Intelligence
dc.subject.keywordsComputer Science, Information Systems
dc.subject.keywordsComputer Science, Theory & Methods
dc.titleEnabling privacy-preserving sharing of genomic data for GWASs in decentralized networks
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationZhang, Y; Zhong, M; Zhao, X; Curtis, C; Li, X; Chen, C, Enabling privacy-preserving sharing of genomic data for GWASs in decentralized networks, WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining, 2019, pp. 204-212
dc.date.updated2021-08-27T03:12:05Z
gro.hasfulltextNo Full Text
gro.griffith.authorChen, Chen


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