DocumentCode
178615
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
fYear
2014
fDate
24-28 Aug. 2014
Firstpage
3594
Lastpage
3599
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition (ICPR), 2014 22nd International Conference on
Conference_Location
Stockholm
ISSN
1051-4651
Type
conf
DOI
10.1109/ICPR.2014.618
Filename
6977330
Link To Document