Anonymization technique through record elimination to preserve privacy of published data

Abstract

The term Data Privacy is associated with data collection and dissemination of data. Privacy issues arise in various area such as health care, intellectual property, biological data etc. It is one of the challenging issues when sharing or publishing the data between one to many sources for research purpose and data analysis. Sensitive information of data owners must be protected. There are two kinds of major attacks against privacy namely record linkage and attribute linkage attacks Earlier, researchers have proposed new methods namely k-anonymity, l-dlverslty, t-closeness for data privacy. K-anonymity method preserves the privacy against record linkage attack alone. It fails to address attribute linkage attack. l-diversity method overcomes the drawback of k-anonymity method. But it fails to address identity disclosure attack and attribute disclosure attack in some exceptional cases. t-closeness method preserves the privacy against attribute linkage attack but not identity disclosure attack. But it computational complexity is large. In this paper, the authors propose a new method to preserve the privacy of individuals' sensitive data from record and attribute linkage attacks. In the proposed method, privacy preservation is achieved through generalization of quasi identifier by setting range values and record elimination. The proposed method is implemented and tested with various data sets.