TY  - JOUR
T1  - Exploring Utility of Extended Misusability Measure for Data Publications
AU - Pradeep Kumar, J. AU - Kumar, A. Udaya AU - Ravi, T. 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 8
SP  - 2138
EP  - 2142
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.2138.2142
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.2138.2142
KW  - Anonymization
KW  -misusability measure
KW  -privacy preserving knowledge discovery
KW  -privacy preserving data publishing
KW  -significant
AB  - 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.
ER  -