Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Preserving Private Knowledge in Frequent Pattern Mining Hong Kong, China December 18-December 22 ISBN: 0-7695-2702-7
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.
Citation:
Zhihui Wang, Wei Wang, Baile Shi, S. H. Boey, "Preserving Private Knowledge in Frequent Pattern Mining," icdmw, pp.530-534, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||