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Soft Computing

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01-07-2011 | Original Paper

An evolutionary approach to enhance data privacy

Authors: Javier Jiménez, Jordi Marés, Vicenç Torra

Published in: Soft Computing | Issue 7/2011

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Abstract

Dissemination of data with sensitive information about individuals has an implicit risk of unauthorized disclosure. Perturbative masking methods propose the distortion of the original data sets before publication, tackling a difficult tradeoff between data utility (low information loss) and protection against disclosure (low disclosure risk). In this paper, we describe how information loss and disclosure risk measures can be integrated within an evolutionary algorithm to seek new and enhanced masking protections for continuous microdata. The proposed technique constitutes a hybrid approach that combines state-of-the-art protection methods with an evolutionary algorithm optimization. We also provide experimental results using three data sets in order to illustrate and empirically evaluate the application of this technique.

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The history of these kinds of algorithms goes into the early 1950s and is associated to different scientists, completely independent from each other (Michalewicz and Fogel 2004). Each procedure was slightly different, and some got names like evolutionary computation (Back et al. 2000), genetic algorithms (Holland 1975) or evolution strategies (Rechenberg 1970; Schwefel 1981). Through time, the different approaches borrowed, exchanged and modified ideas. Then the term evolutionary algorithm emerged to describe any of these algorithms, which is the denomination that we follow in this paper.

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Title: An evolutionary approach to enhance data privacy
Authors: Javier Jiménez
Jordi Marés
Vicenç Torra
Publication date: 01-07-2011
Publisher: Springer-Verlag
Published in: Soft Computing / Issue 7/2011
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-010-0672-1

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