A Comprehensive Review of Privacy Preserving Data Publishing (PPDP) Algorithms for Multiple Sensitive Attributes (MSA)

Abstract

Privacy preserving data publishing (PPDP) provides a suite of anonymization algorithms and tools that aim to balance the privacy of sensitive attributes and utility of the published data. In this domain, extensive work has been carried out to preserve the privacy of single sensitive attributes. Since most of the data obtained from any domain includes multiple sensitive attributes (MSAs), there is a greater need to preserve it. The data sets with multiple sensitive attributes allow one to perform effective data analysis, research, and predictions. Hence, it is important to investigate privacy preserving algorithms for multiple sensitive attributes, which leads to higher utilization of the data. This chapter presents the effectiveness and comparative analysis of PPDP algorithms for MSAs. Specifically, the chapter focuses on privacy and utility goals and illustrates implications of the overall study, which promotes the development of effective privacy preservation techniques for MSAs.