NDLI: Quantification of private information leakage from phenotype-genotype data: linking attacks.

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Quantification of private information leakage from phenotype-genotype data: linking attacks.

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Quantification of private information leakage from phenotype-genotype data: linking attacks.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Harmanci, Arif Gerstein, Mark
Description	Country affiliation: United States Author Affiliation: Harmanci A ( Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA.); Gerstein M ( Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA.)
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.
File Format	HTM / HTML
ISSN	15487091
e-ISSN	15487105
DOI	10.1038/nmeth.3746
Journal	Nature Methods
Issue Number	3
Volume Number	13
Language	English
Publisher	Nature Publishing Group
Publisher Date	2016-03-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Discipline Clinical Laboratory Techniques Anonymous Testing Computer Security Confidentiality Data Mining Databases, Genetic Genetic Privacy Organization & Administration Genotype Phenotype Quantitative Trait Loci Genetics
Content Type	Text
Resource Type	Article
Subject	Cell Biology Biochemistry Molecular Biology Biotechnology

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Quantification of Private Information Leakage from Phenotype-Genotype Data: Linking Attacks

Genotype matrix mapping: searching for quantitative trait loci interactions in genetic variation in complex traits.

Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study

Phen-Gen: combining phenotype and genotype to analyze rare disorders.

Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype–phenotype interactions

Efficient association mapping of quantitative trait loci with selective genotyping.

Mapping of quantitative trait loci for fire blight resistance in the apple cultivars ‘Florina’ and ‘Nova Easygro’

Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB

Questioning the need for anonymous genetic counseling and testing.

Quantification of private information leakage from phenotype-genotype data: linking attacks.

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Quantification of Private Information Leakage from Phenotype-Genotype Data: Linking Attacks

Genotype matrix mapping: searching for quantitative trait loci interactions in genetic variation in complex traits.

Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study

Phen-Gen: combining phenotype and genotype to analyze rare disorders.

Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype–phenotype interactions

Efficient association mapping of quantitative trait loci with selective genotyping.

Mapping of quantitative trait loci for fire blight resistance in the apple cultivars ‘Florina’ and ‘Nova Easygro’

Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB

Questioning the need for anonymous genetic counseling and testing.

Quantification of private information leakage from phenotype-genotype data: linking attacks.