Abstract: The field of Preserving Privacy in Data Mining is gaining momentum in the recent times as the data sets are more open towards mining by organizations and academic institutes. Ensuring privacy in data before publishing it to wider audience is always an open challenge. There have been many techniques evolved to exploit the perturbed data and get some sensitive knowledge. In the process of ensuring more privacy, the data perturbation techniques also became complex and more distortive in nature. The Data Utility is level of usefulness of the distorted data. The study of data utility comes into play as the distortion level increases. In this paper we are going to propose a pre and post perturbation analysis for measuring the data utility and using this as an input to choose the balance the Data Utility and Privacy in the datasets. This paper primarily focuses the privacy and data utility for datasets which are relational in nature.
Keywords: Privacy Preserving, Data Mining, Tuning Data Utility.
Title: FINDU: A Methodology for Finding and Fine Tuning the Data Utility for Privacy Preserving Data Mining
Author: N. Sridhar, Dr Y. Srinivas
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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