Title :
Quality Evaluation of an Anonymized Dataset
Author :
Fletcher, S. ; Islam, M.Z.
Author_Institution :
Sch. of Comput. & Math., Charles Sturt Univ., Bathurst, NSW, Australia
Abstract :
In this study we argue that the traditional approach of evaluating the information quality of an anonymized (or otherwise modified) dataset is questionable. We propose a novel and simple approach to evaluate the information quality of a modified dataset, and thereby the quality of techniques that modify data. We carry out experiments on eleven datasets and the empirical results strongly support our arguments. We also present some supplementary measures to our approach that provide additional insight into the information quality of modified data.
Keywords :
data mining; pattern classification; anonymized dataset; data mining; empirical analysis; information quality evaluation; modified dataset; Accuracy; Cancer; Data privacy; Decision trees; Muscles; Noise; Testing; anonymization; data mining; data quality; information quality; noise addition; privacy preserving data mining;
Conference_Titel :
Pattern Recognition (ICPR), 2014 22nd International Conference on
Conference_Location :
Stockholm
DOI :
10.1109/ICPR.2014.618