A Survey on Privacy Preserving Approaches in Data Publishing

Abstract

Privacy preserving in data publishing has become one of the most important research topics in data security field and it has become a serious concern in publication of personal data in recent years. How to efficiently protect individual privacy in data publishing is especially critical. Thus, various proposals have been designed for privacy preserving in data publishing. In this paper, we summarize privacy preserving approaches in data publishing and survey current existing techniques, and analyze the advantage and disadvantage of these approaches. We divide these proposals into two categories, one is to achieve the purpose of privacy preserving based on k-anonymity model, and the other is to utilize the methods of probability or statistics to protect data privacy in the case of the statistical properties of the final data and classification properties are unchanged. For example, clustering, randomization approaches. Finally, we discuss the future directions of privacy preserving in data publishing.