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Quantifying Privacy: A Novel Entropy-Based Measure of Disclosure Risk

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Combinatorial Algorithms (IWOCA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8986))

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  • International Workshop on Combinatorial Algorithms

Abstract

It is well recognised that data mining and statistical analysis pose a serious treat to privacy. This is true for financial, medical, criminal and marketing research. Numerous techniques have been proposed to protect privacy, including restriction and data modification. Recently proposed privacy models such as differential privacy and k-anonymity received a lot of attention and for the latter there are now several improvements of the original scheme, each removing some security shortcomings of the previous one. However, the challenge lies in evaluating and comparing privacy provided by various techniques. In this paper we propose a novel entropy based security measure that can be applied to any generalisation, restriction or data modification technique. We use our measure to empirically evaluate and compare a few popular methods, namely query restriction, sampling and noise addition.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW, 2308, Australia

    Mousa Alfalayleh & Ljiljana Brankovic

Authors
  1. Mousa Alfalayleh
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  2. Ljiljana Brankovic
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Correspondence to Ljiljana Brankovic .

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Editors and Affiliations

  1. Faculty of Mathematics and Physics, Charles University, Praha, Czech Republic

    Kratochvíl Jan

  2. University of Newcastle, Newcastle, New South Wales, Australia

    Mirka Miller

  3. University of Minnesota, Duluth Department of Mathematics, Duluth, Minnesota, USA

    Dalibor Froncek

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Alfalayleh, M., Brankovic, L. (2015). Quantifying Privacy: A Novel Entropy-Based Measure of Disclosure Risk. In: Jan, K., Miller, M., Froncek, D. (eds) Combinatorial Algorithms. IWOCA 2014. Lecture Notes in Computer Science(), vol 8986. Springer, Cham. https://doi.org/10.1007/978-3-319-19315-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-19315-1_3

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  • Online ISBN: 978-3-319-19315-1

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