Authors : J. Pradeep Kumar, A. Udaya Kumar and T. Ravi
Abstract: When data is published in the real world it is essential to ensure that privacy is not disclosed and the data is not misused. In our study earlier we proposed an extended misusability measure that helps in finding the probability of misuse of given dataset. The measure takes single or multiple publications as input and generates misusability score. This score determines the level of misusability possible with the given dataset. The misusability leads to possible disclosure of privacy. By computing misusability score, it is possible to anonymize sensitive attributes to achieve privacy preserving data publications and data mining as well. In this study, our aim is to demonstrate the real utility of our extended misusability measure. We proposed a framework with an underlying algorithm to sanitize data before publishing it or before it is subjected to mining. The proposed algorithm employs the measure and determines the need for sanitizing datasets. The algorithm in turn uses K-anonymity which one of the standard sanitization algorithms for preventing privacy attacks on the datasets. We built a prototype application that demonstrates the proof of concept. The empirical results revealed that our misusability measure has significant impact on the privacy preserving data publishing and privacy preserving data mining.
J. Pradeep Kumar, A. Udaya Kumar and T. Ravi, 2017. Exploring Utility of Extended Misusability Measure for Data Publications. Journal of Engineering and Applied Sciences, 12: 2138-2142.