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General Information
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2015 Vol.7(1): 21-28 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2015.V7.924

Measuring Information Quality for Privacy Preserving Data Mining

Sam Fletcher and Md Zahidul Islam
Abstract—In the strive for knowledge discovery in a world of ever-growing data collection, it is important that even if a dataset is altered to preserve people’s privacy, the information in the dataset retains as much quality as possible. In this context, “quality” refers to the accuracy or usefulness of the information retrievable from a dataset. Defining and measuring the loss of information after meeting privacy requirements proves difficult however. Techniques have been developed to measure the information quality of a dataset for a variety of anonymization techniques including Generalization, Suppression, and Randomization. Some measures analyze the data, while others analyze the outputted data mining results from tasks such as Clustering and Classification. This survey discusses a collection of information measures, and issues surrounding their usage and limitations.

Index Terms—Anonymization, data mining, data quality, privacy preserving data mining.

S. Fletcher and M. Z. Islam are with the Center for Research in Complex Systems (CRiCS), School of Computing and Mathematics, Charles Sturt University, Bathurst NSW


Cite:Sam Fletcher and Md Zahidul Islam, "Measuring Information Quality for Privacy Preserving Data Mining," International Journal of Computer Theory and Engineering vol. 7, no. 1, pp. 21-28, 2015.

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