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  • 标题:Private algorithms for the protected in social network search
  • 本地全文:下载
  • 作者:Michael Kearns ; Aaron Roth ; Zhiwei Steven Wu
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2016
  • 卷号:113
  • 期号:4
  • 页码:913-918
  • DOI:10.1073/pnas.1510612113
  • 语种:English
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要:Motivated by tensions between data privacy for individual citizens and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a computational model that distinguishes between parties for whom privacy is explicitly protected, and those for whom it is not (the targeted subpopulation). The goal is the development of algorithms that can effectively identify and take action upon members of the targeted subpopulation in a way that minimally compromises the privacy of the protected, while simultaneously limiting the expense of distinguishing members of the two groups via costly mechanisms such as surveillance, background checks, or medical testing. Within this framework, we provide provably privacy-preserving algorithms for targeted search in social networks. These algorithms are natural variants of common graph search methods, and ensure privacy for the protected by the careful injection of noise in the prioritization of potential targets. We validate the utility of our algorithms with extensive computational experiments on two large-scale social network datasets.
  • 关键词:data privacy ; social networks ; counterterrorism
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