(α, k)-anonymity based privacy preservation by lossy join

Abstract

Privacy-preserving data publication for data mining is to protect sensitive information of individuals in published data while the distortion to the data is minimized. Recently, it is shown that (α, k)-anonymity is a feasible technique when we are given some sensitive attribute(s) and quasi-identifier attributes. In previous work, generalization of the given data table has been used for the anonymization. In this paper, we show that we can project the data onto two tables for publishing in such a way that the privacy protection for (α, k)-anonymity can be achieved with less distortion. In the two tables, one table contains the undisturbed non-sensitive values and the other table contains the undisturbed sensitive values. Privacy preservation is guaranteed by the lossy join property of the two tables. We show by experiments that the results are better than previous approaches. © Springer-Verlag Berlin Heidelberg 2007.