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Open AccessJournal ArticleDOI

Scalable privacy-preserving data sharing methodology for genome-wide association studies.

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TLDR
This work extends the methods developed in Uhler et al. (2013) for releasing differentially-private χ(2)-statistics by allowing for arbitrary number of cases and controls, and provides a new interpretation by assuming the controls' data are known, which is a realistic assumption because some GWAS use publicly available data as controls.
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This article is published in Journal of Biomedical Informatics.The article was published on 2014-08-01 and is currently open access. It has received 159 citations till now. The article focuses on the topics: Differential privacy.

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Citations
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Journal ArticleDOI

Routes for breaching and protecting genetic privacy.

TL;DR: An overview of genetic privacy breaching strategies is presented, outlining the principles of each technique, the underlying assumptions, and their technological complexity and maturation, as well as highlighting different cases that are relevant to genetic applications.
Journal ArticleDOI

Technical Privacy Metrics: A Systematic Survey

TL;DR: A survey of privacy metrics can be found in this article, where the authors discuss a selection of over 80 privacy metrics and introduce categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection.
Journal ArticleDOI

Privacy in the Genomic Era

TL;DR: An enumeration of the challenges for genome data privacy is enumerated and a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward is presented.
Journal ArticleDOI

Exposed! A Survey of Attacks on Private Data

TL;DR: This survey focuses on attacking aggregate data, such as statistics about how many individuals have a certain disease, genetic trait, or combination thereof, and considers two types of attacks: reconstruction attacks, which approximately determine a sensitive feature of all the individuals covered by the dataset, and tracing attacks,Which determine whether or not a target individual's data are included in the dataset.
Posted Content

Privacy in the Genomic Era

TL;DR: The problem of genome data privacy is at the crossroads of computer science, medicine, and public policy as discussed by the authors, and the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policies.
References
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Journal ArticleDOI

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

Paul Burton, +195 more
- 07 Jun 2007 - 
TL;DR: This study has demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in theBritish population is generally modest.
Book ChapterDOI

Calibrating noise to sensitivity in private data analysis

TL;DR: In this article, the authors show that for several particular applications substantially less noise is needed than was previously understood to be the case, and also show the separation results showing the increased value of interactive sanitization mechanisms over non-interactive.
Journal Article

Calibrating noise to sensitivity in private data analysis

TL;DR: The study is extended to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f, which is the amount that any single argument to f can change its output.
Journal ArticleDOI

Genomic control for association studies.

TL;DR: The performance of the genomic control method is quite good for plausible effects of liability genes, which bodes well for future genetic analyses of complex disorders.
Proceedings ArticleDOI

Mechanism Design via Differential Privacy

TL;DR: It is shown that the recent notion of differential privacv, in addition to its own intrinsic virtue, can ensure that participants have limited effect on the outcome of the mechanism, and as a consequence have limited incentive to lie.
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