A Comparative Study of Data Anonymization Techniques

Abstract

In today's digital era, it is a very common practice for organizations to collect data from individual users. The collected data is then stored in multiple databases which contain personally identifiable information (PII). This may lead to a major source of privacy risk for the database. Various privacy preservation techniques have been proposed such as perturbation, anonymization and cryptographic. In this study, five anonymization techniques are compared using the same dataset. In addition to that, this study reviews the strengths and weaknesses of the different technique. In the evaluation of efficiency, suppression is found as the most efficient while swapping is in the last place. It is also revealed that swapping is the most resource-consuming technique while suppressing being less resource consuming. © 2019 IEEE.