Preserving privacy for sensitive values of individuals in data publishing based on a new additive noise approach

Abstract

The disclosure of individuals' sensitive values is a potentially serious problem in privacy preserving data publishing. The popular ℓ-diversity model is able to prevent victim' sensitive value from being revealed with a certainty higher than 1/ℓ. But, the anonymized tables with ℓ-diversity generated by the methods, such as generalization, anatomy and slicing, are all vulnerable to the attacker with some individuals' sensitive values. In this paper we propose a new approach to publish anonymized table with ℓ-diversity against the attacker having publishing algorithm and some individuals' sensitive values. Such an approach replaces the sensitive attribute (SA) value of each record with an values set consisted of the real SA value and several random noise values. The method can be applied to protect numerical and nominal SA, existing additive noise only is used to protect the numerical SA of anonymized table. Comparing with randomization, our method retains the frequency distribution of SA values of original data and retains more correlations (between SA and other attributes). Experimental evaluation shows that our method preserves more correlations than that of generalization, slicing and anatomy.