Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Anonymization is a practical solution for preserving user’s privacy in data publishing. Data owners such as hospitals, banks, social network (SN) service providers, and insurance companies anonymize their before publishing it to protect the of users whereas anonymous remains useful legitimate information consumers. Many anonymization models, algorithms, frameworks, prototypes have been proposed/developed (PPDP). These models/algorithms users’ which mainly form tables or graphs depending upon owners. It paramount importance provide good perspectives whole area involving both tabular SN data, recent researches. In this paper, we presents comprehensive survey about (i.e., graphs) relational tabular) techniques used PPDP. We systematically categorize existing into structural anonymization, present an up date thorough review on metrics evaluation. Our aim deeper insights PPDP problem possible attacks that can be launched sanitized published different actors involved scenario, major differences amount private contained respectively. various representative methods proposed solve problems application-specific scenarios SNs. Furthermore, highlight re-identification by malevolent adversaries re-identify people uniquely from preserved data. Additionally, discuss challenges anonymizing elaborate promising research directions. To best our knowledge, first work cover provides solid foundation future studies field.