title: Quantification of private information leakage from phenotype-genotype data: linking attacks.
abstract: Studies on genomic privacy have traditionally focused on identifying individuals using DNA variants. In contrast, molecular phenotype data, such as gene expression levels, are generally assumed to be free of such identifying information. Although there is no explicit genotypic information in phenotype data, adversaries can statistically link phenotypes to genotypes using publicly available genotype-phenotype correlations such as expression quantitative trait loci (eQTLs). This linking can be accurate when high-dimensional data (i.e., many expression levels) are used, and the resulting links can then reveal sensitive information (for example, the fact that an individual has cancer). Here we develop frameworks for quantifying the leakage of characterizing information from phenotype data sets. These frameworks can be used to estimate the leakage from large data sets before release. We also present a general three-step procedure for practically instantiating linking attacks and a specific attack using outlier gene expression levels that is simple yet accurate. Finally, we describe the effectiveness of this outlier attack under different scenarios.
authors.affiliation: Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA.|Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA.
authors.forename: Arif
authors.initials: A
authors.lastname: Harmanci
authors.affiliation: Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA.|Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA.|Department of Computer Science, Yale University, New Haven, Connecticut, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Gerstein
mesh_terms: D035041:Anonymous Testing / Q000379:methods*; D016494:Computer Security; D003219:Confidentiality; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D030661:Genetic Privacy / Q000458:organization & administration*; D005838:Genotype; D006801:Humans; D010641:Phenotype; D040641:Quantitative Trait Loci / Q000235:genetics
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