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dc.contributor.authorDeznabi, I.en_US
dc.contributor.authorMobayen, M.en_US
dc.contributor.authorJafari, N.en_US
dc.contributor.authorTastan, O.en_US
dc.contributor.authorAyday, E.en_US
dc.date.accessioned2019-02-12T08:28:13Z
dc.date.available2019-02-12T08:28:13Z
dc.date.issued2018en_US
dc.identifier.issn1545-5963
dc.identifier.urihttp://hdl.handle.net/11693/49301
dc.description.abstractAbstract—Individuals (and their family members) share (partial) genomic data on public platforms. However, using special characteristics of genomic data, background knowledge that can be obtained from the Web, and family relationship between the individuals, it is possible to infer the hidden parts of shared (and unshared) genomes. Existing work in this field considers simple correlations in the genome (as well as Mendel’s law and partial genomes of a victim and his family members). In this paper, we improve the existing work on inference attacks on genomic privacy. We mainly consider complex correlations in the genome by using an observable Markov model and recombination model between the haplotypes. We also utilize the phenotype information about the victims. We propose an efficient message passing algorithm to consider all aforementioned background information for the inference. We show that the proposed framework improves inference with significantly less information compared to existing work.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE - ACM Transactions on Computational Biology and Bioinformaticsen_US
dc.relation.isversionofhttp://doi.org/10.1109/TCBB.2017.2709740en_US
dc.subjectPrivacyen_US
dc.subjectSecurityen_US
dc.subjectGenomic privacyen_US
dc.subjectİnference attacksen_US
dc.titleAn inference attack on genomic data using kinship, complex correlations, and phenotype informationen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage1333en_US
dc.citation.epage1342en_US
dc.citation.volumeNumber15en_US
dc.citation.issueNumber4en_US
dc.identifier.doi10.1109/TCBB.2017.2709740en_US
dc.publisherIEEEen_US
dc.identifier.eissn1557-9964


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