Enabling privacy-preserving sharing of genomic data for GWASs in decentralized networks
Author(s)
Zhang, Y
Zhong, M
Zhao, X
Curtis, C
Li, X
Chen, C
Griffith University Author(s)
Year published
2019
Metadata
Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Conference Title
WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining
Subject
Genomics
Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Theory & Methods