Redefining Genomic Privacy: Trust and Empowerment

Erlich, Yaniv; Williams, James B.; Glazer, David; Yocum, Kenneth; Farahany, Nita A.; Olson, Maynard V.; Narayanan, Arvind; Stein, Lincoln; Witkowski, Jan; Kain, Robert C.

doi:10.1371/journal.pbio.1001983

Cited by 98 publications

(87 citation statements)

References 14 publications

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“…In place of an absolute guarantee against harm, or a claim that re-identification is impossible, it rests on securing broad consent to data sharing (or using a platform that allows for dynamic and granular consent) and continuing efforts to minimize risk (for example, where linkages across records or datasets are critically important, using non-identifying alphanumeric codes to approximate the privacy protection associated with anonymization). Other features include new forms of governance that facilitate ongoing participant engagement, transparency as a means of building trust and as a mark of respect (including transparency about international data sharing), and accountability mechanisms (including sanctions against those who fail to take appropriate steps to secure data or who use data in ways that are not authorized) [4,26–28]. …”

Section: Basis For Privacy and Security Concernsmentioning

confidence: 99%

Beyond Our Borders? Public Resistance to Global Genomic Data Sharing

2016

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Prospects have never seemed better for a truly global approach to science to improve human health, with leaders of national initiatives laying out their vision of a worldwide network of related projects. An extensive literature addresses obstacles to global genomic data sharing, yet a series of public polls suggests that the scientific community may be overlooking a significant barrier: potential public resistance to data sharing across national borders. In several large United States surveys, university researchers in other countries were deemed the least acceptable group of data users, and a just-completed US survey found a marked increase in privacy and security concerns related to data access by non-US researchers. Furthermore, diminished support for sharing beyond national borders is not unique to the US, although the limited data from outside the US suggest variation across countries as well as demographic groups. Possible sources of resistance include apprehension about privacy and security protections. Strategies for building public support include making the affirmative case for global data sharing, addressing privacy, security, and other legitimate concerns, and investigating public concerns in greater depth.

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Section: Basis For Privacy and Security Concernsmentioning

confidence: 99%

Beyond Our Borders? Public Resistance to Global Genomic Data Sharing

2016

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“…In genomic privacy, it is necessary to consider the basic premise of sharing any type of information: there is always an amount of sensitive information leakage in every released dataset 19 . It is therefore essential for the genomic data sharing and publishing mechanisms to incorporate statistical quantification methods to objectively quantify risk estimates before the datasets are released.…”

Section: Discussionmentioning

confidence: 99%

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

Harmanci

Gerstein

2016

Nat Methods

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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 free of such identifying information. Although there is no explicit genotypic information in them, adversaries can statistically link phenotypes to genotypes using publicly available genotype-phenotype correlations, for instance, expression quantitative trait loci (eQTLs). This linking can be accurate when high-dimensional data (many expression levels) are used, and the resulting links can then reveal sensitive information, for example, an individual having cancer. Here, we develop frameworks for quantifying the leakage of individual characterizing information from phenotype datasets. These can be used for estimating the leakage from large datasets 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.

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“…Where an agronomist sees the challenges of feeding billions of people (McCouch et al 2013), we see many carriers of every nonlethal de novo mutation (Kong et al 2012) and homozygotes or compound heterozygotes for loss-of-function alleles in thousands of genes (MacArthur et al 2012). Of course, nontrivial obstacles must be overcome, particularly to craft data sharing and research protocols that truly engage, inform, protect, and respect research participants (Greely 2007;Erlich et al 2014), but the availability of millions of genomes attached to at least some phenotypic information is realistic in the coming years.…”

Section: Toward More General and Quantitative Variant Classificationmentioning

confidence: 99%

Parlez-vous VUS?

Cooper

2015

Genome Res.

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Human genome sequencing is routine and will soon be a staple in research and clinical genetics. However, the promise of sequencing is often just that, with genome data routinely failing to reveal useful insights about disease in general or a person's health in particular. Nowhere is this chasm between promise and progress more evident than in the designation, “variant of uncertain significance” (VUS). Although it serves an important role, careful consideration of VUS reveals it to be a nebulous description of genomic information and its relationship to disease, symptomatic of our inability to make even crude quantitative assertions about the disease risks conferred by many genetic variants. In this perspective, I discuss the challenge of “variant interpretation” and the value of comparative and functional genomic information in meeting that challenge. Although already essential, genomic annotations will become even more important as our analytical focus widens beyond coding exons. Combined with more genotype and phenotype data, they will help facilitate more quantitative and insightful assessments of the contributions of genetic variants to disease.

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Redefining Genomic Privacy: Trust and Empowerment

Abstract: Current models of protecting human subjects create a zero-sum game of privacy versus data utility. We propose shifting the paradigm to techniques that facilitate trust between researchers and participants.

Cited by 98 publications

References 14 publications

Beyond Our Borders? Public Resistance to Global Genomic Data Sharing

Beyond Our Borders? Public Resistance to Global Genomic Data Sharing

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

Parlez-vous VUS?

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