Privacy-preserving tabular data publishing: A comprehensive evaluation from web to cloud

Abstract

The amount of data collected by various organizations about individuals is continuously increasing. This includes diverse data sources often for data of high dimensionality. Most of these data are stored in tabular format and can include sensitive content. Preserving data privacy is an essential task in order to allow such data to be published for different research and analysis purposes. In this context, Privacy-Preserving Tabular Data Publishing (PPTDP) has drawn considerable attention, where different approaches have been proposed to preserve the privacy of individuals' tabular data. Such data can include Single Sensitive Attributes (SSA) or Multiple Sensitive Attributes (MSA) or come from data streams. In this paper, we conduct a comprehensive study to analyze and evaluate the main different data anonymization approaches that have been introduced in PPTDP. The study investigates the three broad areas of research: SSA, MSA and data streams. A detailed criticism is presented to highlight the strengths and the weaknesses of each approach including their deployment in the cloud and Internet of Things (IoT) environments. A research gap analysis is discussed with a focus on capturing current state of the art in this field in order to highlight the future directions that can be considered.