An information theoretic framework for web inference detection

  • Authors:

  • Hoi Le Thi;Reihaneh Safavi-Naini

  • Affiliations:

  • University of Calgary, Calgary, AB, Canada;University of Calgary, Calgary, AB, Canada

  • Venue:
  • Proceedings of the 5th ACM workshop on Security and artificial intelligence
  • Year:
  • 2012

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Abstract

Document redaction is widely used to protect sensitive information in published documents. In a basic redaction system, sensitive and identifying terms are removed from the document. Web-based inference is an attack on redaction systems whereby the redacted document is linked with other publicly available documents to infer the removed parts. Web-based inference also provides an approach for detecting unwanted inferences and so constructing secure redaction systems. Previous works on web-based inference used general keyword extraction methods for document representation. We propose a systematic approach, based on information theoretic concepts and measures, to rank the words in a document for purpose of inference detection. We extend our results to the case of multiple sensitive words and propose a metric that takes into account possible relationship of the sensitive words and results in an effective and efficient inference detection system. Using a number of experiments we show that our approach, when used for document redaction, substantially reduce the number of inferences that are left in a document. We describe our approach, present the experiment results, and outline future work.