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Reasoning about unstructured data de-identification

Author

Listed:
  • Thaine, Patricia

    (PhD Candidate, University of Toronto Co-Founder & CEO, Private AI, Canada)

  • Penn, Gerald

    (Professor of Computer Science, University of Toronto Co-Founder & Chief Science Officer, Private AI, Canada)

Abstract

We frame the problem of de-identifying unstructured text within the greater landscape of privacy-enhancing technologies. We then cover what sort of background knowledge can be gained from only stylistic information about a written document and how we can use research on authorship attribution and author profiling to improve our understanding about the sorts of inferences that can be made from an otherwise de-identified text. Finally, we provide a risk score for determining the likelihood that a message will be attributed to a particular author within a dataset using only author profiling tools.

Suggested Citation

  • Thaine, Patricia & Penn, Gerald, 2020. "Reasoning about unstructured data de-identification," Journal of Data Protection & Privacy, Henry Stewart Publications, vol. 3(3), pages 299-309, June.
    • Handle: RePEc:aza:jdpp00:y:2020:v:3:i:3:p:299-309
    as

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    More about this item

    Keywords

    anonymisation; de-identification; authorship attribution; author profiling; unstructured data; risk;
    All these keywords.

    JEL classification:

    • K2 - Law and Economics - - Regulation and Business Law

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