A Sensitive Attribute Based Clustering Method for k-Anonymization

Abstract

In medical organizations large amount of personal data are collected and analyzed by the data miner or researcher, for further perusal. However, the data collected may contain sensitive information such as specific disease of a patient and should be kept confidential. Hence, the analysis of such data must ensure due checks that ensure protection against threats to the individual privacy. In this context, greater emphasis has now been given to the privacy preservation algorithms in data mining research. One of the approaches is anonymization approach that is able to protect private information; however, valuable information can be lost. Therefore, the main challenge is how to minimize the information loss during an anonymization process. The proposed method is grouping similar data together based on sensitive attribute and then anonymizes them. Our experimental results show the proposed method offers better outcomes with respect to information loss and execution time.

Comprehensive Survey on Big Data Privacy Protection

“…However, transforming data or anonymizing individuals may minimize the utility of the transferred data and lead to inaccurate knowledge [12], [20]. Hence, numerous endeavors have been dedicated to privacy, which involve the preservation of individuals' information using data mining algorithms, to avert the disclosure of individuals' identities or sensitive data in the course of knowledge discovery [21]. This paradigm is referred to as PPDM.…”

“…PPDM has recently garnered considerable interest among academics and designers. Consequently, several methods have been developed to protect privacy or far-reaching policies have been imposed for sensitive data protection [12], [21], [25]. The form of privacy varies depending on the data used and the way they are used; hence, many methods are used to provide privacy [25].…”

Feature Based Data Anonymization for High Dimensional Data

“…Hiding data reduces the utility of the data, while disclosing the data reduces privacy. During the anonymization process there is a tradeoff between privacy and information loss (Bhaladhare & Jinwala, 2012). Different anonymization levels leads to different amounts of information loss (Gal, Tucker, Gangopadhyay, & Chen, 2014).…”