KC-Slice: A dynamic privacy-preserving data publishing technique for multisensitive attributes

Abstract

Privacy preservation methods for anonymizing multiple sensitive attributes (MSA) data in the field of privacy-preserving data publishing (PPDP) mostly seek enforcement of the -diversity privacy model on MSA coupled with quasi-identifier (QID) generalization and tuple suppression, resulting in high data degradation of the published releases. Most existing work produces static releases that are not dynamic and web-based. In this article, we propose KC-Slice, which is amodified LKC-privacy model and slicing technique, for anonymizing MSA data dynamically, to produce releases that preserve the dataset content from most attack models and reduce data degradation, through cell suppression and QID random permutation. Experimental results and evaluation using data metrics and information entropy show remarkable reduction in data degradation and suppression ratio.