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Privacy Violation and Detection Using Pattern Mining Techniques

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  • Bhattacharya Jaijit
  • Dass, Rajanish
  • Kapoor Vishal
  • Chakraborti Debamitro
  • Gupta S K

Abstract

Privacy, its violations and techniques to bypass privacy violation have grabbed the centre-stage of both academia and industry in recent months. Corporations worldwide have become conscious of the implications of privacy violation and its impact on them and to other stakeholders. Moreover, nations across the world are coming out with privacy protecting legislations to prevent data privacy violations. Such legislations however expose organizations to the issues of intentional or unintentional violation of privacy data. A violation by either malicious external hackers or by internal employees can expose the organizations to costly litigations. In this paper, we propose PRIVDAM; a data mining based intelligent architecture of a Privacy Violation Detection and Monitoring system whose purpose is to detect possible privacy violations and to prevent them in the future. Experimental evaluations show that our approach is scalable and robust and that it can detect privacy violations or chances of violations quite accurately. Please contact the author for full text at rajanish@iima.ac.in

Suggested Citation

  • Bhattacharya Jaijit & Dass, Rajanish & Kapoor Vishal & Chakraborti Debamitro & Gupta S K, 2005. "Privacy Violation and Detection Using Pattern Mining Techniques," IIMA Working Papers WP2005-07-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    • Handle: RePEc:iim:iimawp:wp01888
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    File URL: https://www.iima.ac.in/sites/default/files/rnpfiles/2005-07-01rajnish.pdf
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    References listed on IDEAS

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    1. Scott D. Watkins & Patrick L. Anderson, 2009. "Pennsylvania," Palgrave Macmillan Books, in: Scott D. Watkins & Patrick L. Anderson (ed.), The State Economic Handbook 2010, pages 190-194, Palgrave Macmillan.
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