Infringement of Individual Privacy via Mining Differentially Private GWAS Statistics

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• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9784
• 期：1
• 页码：355-366
• 全文大小：285 KB
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• 作者单位：Yue Wang (18)
  Jia Wen (18)
  Xintao Wu (19)
  Xinghua Shi (18)
  
  18. University of North Carolina at Charlotte, Charlotte, NC, USA
  19. University of Arkansas, Fayetteville, AR, USA
  
• 丛书名：Big Data Computing and Communications
• ISBN：978-3-319-42553-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9784

文摘

Individual privacy in genomic era is becoming a growing concern as more individuals get their genomes sequenced or genotyped. Infringement of genetic privacy can be conducted even without raw genotypes or sequencing data. Studies have reported that summary statistics from Genome Wide Association Studies (GWAS) can be exploited to threat individual privacy. In this study, we show that even with differentially private GWAS statistics, there is still a risk for leaking individual privacy. Specifically, we constructed a Bayesian network through mining public GWAS statistics, and evaluated two attacks, namely trait inference attack and identity inference attack, for infringement of individual privacy not only for GWAS participants but also regular individuals. We used both simulation and real human genetic data from 1000 Genome Project to evaluate our methods. Our results demonstrated that unexpected privacy breaches could occur and attackers can derive identity information and private information by utilizing these algorithms. Hence, more methodological studies should be invested to understand the infringement and protection of genetic privacy.