Privacy preservation in releasing patient data

Xiaoping Liu, Xiao Bai Li, Luvai Motiwalla, Wenjun Li, Hua Zheng, Patricia Durkin Franklin

Research output: Contribution to conferencePaperpeer-review

Abstract

When patient data are shared for studying a specific disease, a privacy disclosure occurs as long as an individual is known to be in the shared data. Individuals in such specific disease data are thus subject to higher disclosure risk than those in datasets with different diseases. This problem has been overlooked in privacy research and practice. In this study, we analyze disclosure risks for this problem and identify appropriate risk measures. An efficient algorithm is developed for anonymizing the data. An experimental study is conducted to demonstrate the effectiveness of the proposed approach.

Original languageEnglish (US)
StatePublished - Jan 1 2016
Event22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016 - San Diego, United States
Duration: Aug 11 2016Aug 14 2016

Other

Other22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016
CountryUnited States
CitySan Diego
Period8/11/168/14/16

Keywords

  • Patient data
  • Privacy
  • Risk

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems

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