Privacy Preserving Classification with Emerging Patterns
- Piotr Andruszkiewicz
In privacy preserving classification, when data is stored in a centralized database and distorted using a randomization-based technique, we have information loss and reduced accuracy of classification. This paper presents a new approach to privacy preserving classification for centralized data based on Emerging Patterns. The presented solution gives higher accuracy of classification than a decision tree proposed in the literature, especially for high privacy. Effectiveness of this solution has been tested on real data sets and presented in this paper.
- Record ID
- Saygin Yucel, Yucel Saygin Jeffrey Xu Yu, Xu Yu Jeffrey Kargupta Hillol Hillol Kargupta [et al.] (eds.): IEEE International Conference on Data Mining, 2009, CPS, 675 p., ISBN 978-1-4244-5384-9. DOI:10.1109/ICDMW.2009.118 Opening in a new tab
- Keywords in English
- centralized database, data mining, data privacy, data sets, decision tree, decision trees, emerging patterns, pattern classification, privacy preserving classification, randomization-based technique
- DOI:10.1109/ICDMW.2009.82 Opening in a new tab
- http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5360520 Opening in a new tab
- (en) English
- Score (nominal)
- Publication indicators
- = 3; = 4
- Citation count
- Uniform Resource Identifier
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or PerishOpening in a new tab system.