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Personal Privacy Protection in Time of Big Data

  • Marina Sokolova
  • Stan Matwin
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 605)

Abstract

Personal privacy protection increasingly becomes a story of privacy protection in electronic data format. Personal privacy protection also becomes a showcase of advantages and challenges of Big Data phenomenon. Accumulation of massive data volumes combined with development of intelligent Data Mining algorithms allows more data being analysed and linked. Unintended consequences of Big Data analytics include increasing risks of discovery new information about individuals. There are several approaches to protect privacy of individuals in the large data sets, privacy-preserving Data Mining being an example. In this paper, we discuss content-aware prevention of data leaks. We concentrate on protection of personal health information (PHI), arguably the most vulnerable type of personal information. This paper discusses the applied methods and challenges which arise when we want to hold health information private. PHI leak prevention on the Web and on online social networks is our case study.

Keywords

Natural Language Processing Online Community Privacy Protection Health Care Organization Identifiable Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  2. 2.Institute for Big Data AnalyticsDalhousie UniversityDalhousieCanada
  3. 3.Institute of Computer Science, Polish Academy of SciencesWarsawPoland
  4. 4.Faculty of MedicineUniversity of OttawaOttawaCanada

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