The Community for Technology Leaders
2013 IEEE 13th International Conference on Data Mining (2005)
Houston, Texas
Nov. 27, 2005 to Nov. 30, 2005
ISSN: 1550-4786
ISBN: 0-7695-2278-5
pp: 645-648
Yücel Saygin , Sabanci University
Mehmet Keskinöz , Sabanci University
Ali Inan , Sabanci University
Ayça Azgin Hintoğlu , Sabanci University
Enterprises have been collecting data for many reasons including better customer relationship management, and high-level decision making. Public safety was another motivation for large-scale data collection efforts initiated by government agencies. However, such widespread data collection efforts coupled with powerful data analysis tools raised concerns about privacy. This is due to the fact that collected data may contain confidential information. One method to ensure privacy is to selectively hide confidential information from the data sets to be disclosed. In this paper, we focus on hiding confidential correlations. We introduce a heuristic to reduce the information loss and propose a blocking method that prevents discovery of confidential correlations while preserving the usefulness of the data set.
Yücel Saygin, Mehmet Keskinöz, Ali Inan, Ayça Azgin Hintoğlu, "Suppressing Data Sets to Prevent Discovery of Association Rules", 2013 IEEE 13th International Conference on Data Mining, vol. 00, no. , pp. 645-648, 2005, doi:10.1109/ICDM.2005.140
95 ms
(Ver 3.3 (11022016))