Reasoning about unstructured data de-identification

Patricia Thaine and Gerald Penn
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Patricia Thaine: PhD Candidate, University of Toronto Co-Founder & CEO, Private AI, Canada
Gerald Penn: Professor of Computer Science, University of Toronto Co-Founder & Chief Science Officer, Private AI, Canada

Journal of Data Protection & Privacy, 2020, vol. 3, issue 3, 299-309

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.

Keywords: anonymisation; de-identification; authorship attribution; author profiling; unstructured data; risk (search for similar items in EconPapers)
JEL-codes: K2 (search for similar items in EconPapers)
Date: 2020
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