Title :
Preserving Private Knowledge in Frequent Pattern Mining
Author :
Wang, Zhihui ; Wang, Wei ; Shi, Baile ; Boey, S.H.
Author_Institution :
Dept. of Comput. & Inf. Technol., Fudan Univ., Shanghai
Abstract :
The knowledge discovered by data mining may contain sensitive information, which may cause potential threats towards privacy and security. In this paper, we address the problem of better preserving private knowledge by proposing an Item-based Pattern Sanitization to prevent the disclosure of private patterns. We also present two strategies to generate a safe and shareable pattern set for preserving private knowledge in frequent pattern mining
Keywords :
data mining; data privacy; pattern recognition; data preservation; pattern mining; private knowledge; Association rules; Conferences; Data mining; Data privacy; Data security; Information security; Information technology; Itemsets; Transaction databases;
Conference_Titel :
Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2702-7
DOI :
10.1109/ICDMW.2006.132
