Detecting Health-Related Privacy Leaks in Social Networks Using Text Mining Tools

  • Kambiz Ghazinour
  • Marina Sokolova
  • Stan Matwin
Conference paper

DOI: 10.1007/978-3-642-38457-8_3

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7884)
Cite this paper as:
Ghazinour K., Sokolova M., Matwin S. (2013) Detecting Health-Related Privacy Leaks in Social Networks Using Text Mining Tools. In: Zaïane O.R., Zilles S. (eds) Advances in Artificial Intelligence. AI 2013. Lecture Notes in Computer Science, vol 7884. Springer, Berlin, Heidelberg

Abstract

In social media, especially in social networks, users routinely share personal information. In such sharing, they might inadvertently reveal some personal health information, an essential part of their private information. In this work, we present a tool for detection of personal health information (PHI) in a social network site, MySpace. We analyze the PHI with the use of two well-known medical resources MedDRA and SNOMED. We introduce a new measure – Risk Factor of Personal Information – that assesses a possibility of a term to disclose personal health information. We synthesize a profile of a potential PHI leak in a social network, and we demonstrate that this task benefits from the emphasis on the MedDRA and SNOMED terms.

Keywords

Medical electronic dictionaries Personal health information Social networks Machine Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kambiz Ghazinour
    • 1
    • 3
  • Marina Sokolova
    • 1
    • 2
    • 3
  • Stan Matwin
    • 1
    • 4
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaCanada
  2. 2.Faculty of MedicineUniversity of OttawaCanada
  3. 3.Electronic Health Information LabCHEO Research InstituteCanada
  4. 4.Faculty of Computer ScienceDalhousie UniversityCanada

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