dc.contributor.author | Zhang, Y | |
dc.contributor.author | Zhong, M | |
dc.contributor.author | Zhao, X | |
dc.contributor.author | Curtis, C | |
dc.contributor.author | Li, X | |
dc.contributor.author | Chen, C | |
dc.date.accessioned | 2021-08-27T03:35:09Z | |
dc.date.available | 2021-08-27T03:35:09Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 9781450359405 | |
dc.identifier.doi | 10.1145/3289600.3290983 | |
dc.identifier.uri | http://hdl.handle.net/10072/407345 | |
dc.description.abstract | The 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.peerreviewed | Yes | |
dc.language | English | |
dc.publisher | ACM Digital Library | |
dc.relation.ispartofconferencename | 12th ACM International Conference on Web Search and Data Mining (WSDM) | |
dc.relation.ispartofconferencetitle | WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining | |
dc.relation.ispartofdatefrom | 2019-02-11 | |
dc.relation.ispartofdateto | 2019-02-15 | |
dc.relation.ispartoflocation | Melbourne, Australia | |
dc.relation.ispartofpagefrom | 204 | |
dc.relation.ispartofpageto | 212 | |
dc.subject.fieldofresearch | Genomics | |
dc.subject.fieldofresearchcode | 310509 | |
dc.subject.keywords | Science & Technology | |
dc.subject.keywords | Computer Science, Artificial Intelligence | |
dc.subject.keywords | Computer Science, Information Systems | |
dc.subject.keywords | Computer Science, Theory & Methods | |
dc.title | Enabling privacy-preserving sharing of genomic data for GWASs in decentralized networks | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dcterms.bibliographicCitation | Zhang, 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.updated | 2021-08-27T03:12:05Z | |
gro.hasfulltext | No Full Text | |
gro.griffith.author | Chen, Chen | |