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Personal Privacy Protection in Time of Big Data

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Challenges in Computational Statistics and Data Mining

Part of the book series: Studies in Computational Intelligence ((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.

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Notes

  1. 1.

    https://www.riskbasedsecurity.com/reports/2013-DataBreachQuickView.pdf.

  2. 2.

    https://www.infoway-inforoute.ca/index.php/news-media/2012-news-releases/use-of-electronic- medical-records-doubled-over-six-years.

  3. 3.

    http://www.computerweekly.com/news/2240215175/UK-shows-biggest-take-up-of-electronic-Health-records-in-Europe.

  4. 4.

    http://www.networkworld.com/news/2009/020209-data-breach.html.

  5. 5.

    http://www.hhs.gov/ocr/privacy/index.html.

  6. 6.

    http://www.e-laws.gov.on.ca/html/statutes/english/elaws_statutes_04p03_e.htm.

  7. 7.

    http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:31995L0046:EN:HTML.

  8. 8.

    https://www.i2b2.org/NLP/DataSets/Main.php.

  9. 9.

    http://pewinternet.org/Reports/2011/Social-Life-of-Health-Info.aspx.

  10. 10.

    http://www.yourprivacy.co.uk/.

  11. 11.

    http://www.internetlivestats.com/internet-users/.

  12. 12.

    http://en.wikipedia.org/wiki/District_of_Columbia_v.Heller.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Marina Sokolova

  2. Institute for Big Data Analytics, Dalhousie University, Dalhousie, Canada

    Marina Sokolova & Stan Matwin

  3. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Stan Matwin

  4. Faculty of Medicine, University of Ottawa, Ottawa, Canada

    Marina Sokolova

Authors
  1. Marina Sokolova
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  2. Stan Matwin
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Correspondence to Stan Matwin .

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Editors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

    Stan Matwin

  2. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, and Warsaw University of Technology, Warsaw, Poland

    Jan Mielniczuk

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Sokolova, M., Matwin, S. (2016). Personal Privacy Protection in Time of Big Data. In: Matwin, S., Mielniczuk, J. (eds) Challenges in Computational Statistics and Data Mining. Studies in Computational Intelligence, vol 605. Springer, Cham. https://doi.org/10.1007/978-3-319-18781-5_18

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  • DOI: https://doi.org/10.1007/978-3-319-18781-5_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18780-8

  • Online ISBN: 978-3-319-18781-5

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