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Probabilistic Information Loss Measures in Confidentiality Protection of Continuous Microdata

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Abstract

Inference control for protecting the privacy of microdata (individual data) should try to optimize the tradeoff between data utility (low information loss) and protection against disclosure (low disclosure risk). Whereas risk measures are bounded between 0 and 1, information loss measures proposed in the literature for continuous data are unbounded, which makes it awkward to trade off information loss for disclosure risk. We propose in this paper to use probabilities to define bounded information loss measures for continuous microdata.

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Acknowledgments

Thanks go to Jordi Castellà for his help in preparing the web form http://vneumann.etse.urv.es/SDC/measures . Also, comments by William Winkler greatly helped improving the presentation of this paper. This work was partly funded by the Spanish Ministry of Science and Technology and the European FEDER Fund under project TIC2001-0633-C03-01 “STREAMOBILE” and also by the Spanish Ministry of Education and Science under project SEG2004-04352-C04-01 “PROPRIETAS”.

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Authors and Affiliations

  1. Department of Computer Engineering and Mathematics, Rovira i Virgili University of Tarragona, Av. Països Catalans 26, E-43007, Tarragona, Catalonia, Spain

    Josep M. Mateo-Sanz, Josep Domingo-Ferrer & Francesc Sebé

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  1. Josep M. Mateo-Sanz
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  2. Josep Domingo-Ferrer
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  3. Francesc Sebé
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Correspondence to Josep M. Mateo-Sanz.

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Geoff Webb

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Mateo-Sanz, J.M., Domingo-Ferrer, J. & Sebé, F. Probabilistic Information Loss Measures in Confidentiality Protection of Continuous Microdata. Data Min Knowl Disc 11, 181–193 (2005). https://doi.org/10.1007/s10618-005-0011-9

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  • DOI: https://doi.org/10.1007/s10618-005-0011-9

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