Expanding Access to Large-Scale Genomic Data While Promoting Privacy: A Game Theoretic Approach

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• 作者：Zhiyu Wan¹ ; ^{zhiyu.wan@vanderbilt.edu} ; Yevgeniy Vorobeychik¹ ; Weiyi Xia¹ ; Ellen Wright Clayton² ; ³ ; ⁴ ; Murat Kantarcioglu⁵ ; Bradley Malin¹ ; ⁶ ; ^{b.malin@vanderbilt.edu}
• 关键词：genomic data privacy ; game theory ; genomic data sharing policy ; summary statistics ; re-identification risk ; adversarial modeling ; genetic algorithm ; sensitivity analysis ; Sequence and Phenotype Integration Exchange ; Electronic Medical Records and Genomics Network
• 刊名：The American Journal of Human Genetics
• 出版年：2017
• 出版时间：2 February 2017
• 年：2017
• 卷：100
• 期：2
• 页码：316-322
• 全文大小：1068 K
• 卷排序：100

文摘

Emerging scientific endeavors are creating big data repositories of data from millions of individuals. Sharing data in a privacy-respecting manner could lead to important discoveries, but high-profile demonstrations show that links between de-identified genomic data and named persons can sometimes be reestablished. Such re-identification attacks have focused on worst-case scenarios and spurred the adoption of data-sharing practices that unnecessarily impede research. To mitigate concerns, organizations have traditionally relied upon legal deterrents, like data use agreements, and are considering suppressing or adding noise to genomic variants. In this report, we use a game theoretic lens to develop more effective, quantifiable protections for genomic data sharing. This is a fundamentally different approach because it accounts for adversarial behavior and capabilities and tailors protections to anticipated recipients with reasonable resources, not adversaries with unlimited means. We demonstrate this approach via a new public resource with genomic summary data from over 8,000 individuals—the Sequence and Phenotype Integration Exchange (SPHINX)—and show that risks can be balanced against utility more effectively than with traditional approaches. We further show the generalizability of this framework by applying it to other genomic data collection and sharing endeavors. Recognizing that such models are dependent on a variety of parameters, we perform extensive sensitivity analyses to show that our findings are robust to their fluctuations.