Scalable privacy-preserving data sharing methodology for genome-wide association studies.
Scalable privacy-preserving data sharing methodology for genome-wide association studies.
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DOI：
10.1016/j.jbi.2014.01.008
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发表时间：
2014-08
影响因子：
4.5
通讯作者：
Uhler, Caroline
中科院分区：
文献类型：
--
作者：
Yu, Fei;Fienberg, Stephen E.;Slavkovic, Aleksandra B.;Uhler, Caroline
关键词：
在Homer等人发表了对全基因组关联研究（GWAS）数据的“攻击”之后，保护GWAS数据库中个人信息的隐私一直是研究人员的主要关注点。传统的统计数据库保密和隐私保护统计方法不能很好地处理全球WAS数据，特别是在保证不与外部信息挂钩方面。由加密社区引入的最近的差分隐私概念是一种方法，它提供了一个严格的隐私定义，并在任意外部信息存在的情况下提供有意义的隐私保证，尽管这些保证可能会在数据效用方面付出沉重的代价。基于这些概念，Uhler等人提出了新的方法来发布聚合GWAS数据，而不会损害个人隐私。我们扩展的方法，允许任意数量的情况下和控制，并释放差异私人等位基因测试统计量的差异私人χ2统计量。我们还提供了一个新的解释，假设控制的数据是已知的，这是一个现实的假设，因为一些GWAS使用公开可用的数据作为控制。我们评估所提出的方法的性能，通过风险效用分析的一个真实的数据集，由威康信托病例控制联盟收集的DNA样本，并比较的方法与约翰逊和Shmatikov提出的不同的私人释放机制。
The protection of privacy of individual-level information in genome-wide association study (GWAS) databases has been a major concern of researchers following the publication of “an attack” on GWAS data by Homer et al.. Traditional statistical methods for confidentiality and privacy protection of statistical databases do not scale well to deal with GWAS data, especially in terms of guarantees regarding protection from linkage to external information. The more recent concept of differential privacy, introduced by the cryptographic community, is an approach that provides a rigorous definition of privacy with meaningful privacy guarantees in the presence of arbitrary external information, although the guarantees may come at a serious price in terms of data utility. Building on such notions, Uhler et al. proposed new methods to release aggregate GWAS data without compromising an individual's privacy. We extend the methods developed in for releasing differentially-private χ2-statistics by allowing for arbitrary number of cases and controls, and for releasing differentially-private allelic test statistics. We also provide a new interpretation by assuming the controls’ data are known, which is a realistic assumption because some GWAS use publicly available data as controls. We assess the performance of the proposed methods through a risk-utility analysis on a real data set consisting of DNA samples collected by the Wellcome Trust Case Control Consortium and compare the methods with the differentially-private release mechanism proposed by Johnson and Shmatikov.
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影响因子：
30.8
作者：
Sankararaman, Sriram;Obozinski, Guillaume;Halperin, Eran
通讯作者：
Halperin, Eran
影响因子：
56.9
作者：
Gymrek, Melissa;McGuire, Amy L.;Erlich, Yaniv
通讯作者：
Erlich, Yaniv
DOI：
10.1214/10-aoas338
发表时间：
2010-09
期刊：
The annals of applied statistics
影响因子：
--
作者：
Wen X;Stephens M
通讯作者：
Stephens M
影响因子：
1.9
作者：
Zheng G;Joo J;Yang Y
通讯作者：
Yang Y
影响因子：
4.5
作者：
Braun R;Rowe W;Schaefer C;Zhang J;Buetow K
通讯作者：
Buetow K