Review of research on k-anonymization technique to transform data for privacy preservation

Abstract

Private information may leak through data disclosure. Although the identifier attributes are suppressed,
re-identification is possible by linking remaining attributes to several sources. Transforming data using the
k-anonymization technique can ensure levels of privacy. This paper reviews the studies and researches of
k-anonymization technique. Firstly, the paper presents basic knowledge, the re-identification problem, related
terminologies, and attribute types. The paper also reviews information loss estimations and transforming
algorithms to achieve k-anonymity property. Lastly, possible attacks are described, and future studies and
researches are suggested.