An evolutionary approach to enhance data privacy

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. © 2010 Springer-Verlag.Partial support by Generalitat de Catalunya (AGAUR, 2009 SGR 7) and by the Spanish MEC (projects ARES CONSOLIDER INGENIO 2010 CSD2007 00004 and e-AEGIS TSI2007 65406-C03-01/02) is acknowledgedPeer Reviewe