doi: 10.7763/IJCTE.2015.V7.924
Measuring Information Quality for Privacy Preserving Data Mining
- Center for Research in Complex Systems (CRiCS), School of Computing and Mathematics, Charles Sturt University, Bathurst NSW
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.
Keywords
- Anonymization
- data mining
- data quality
- privacy preserving data mining
How to 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. https://doi.org/10.7763/IJCTE.2015.V7.924
Copyright & License
Copyright © 2015 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).