An effective approach for hiding sensitive knowledge in data publishing

Authors:
Zhihui Wang;Bing Liu;Wei Wang;Haofeng Zhou;Baile Shi
Affiliations:
Department of Computing and Information Technology, Fudan University, Shanghai, China;Department of Computing and Information Technology, Fudan University, Shanghai, China;Department of Computing and Information Technology, Fudan University, Shanghai, China;Department of Computing and Information Technology, Fudan University, Shanghai, China;Department of Computing and Information Technology, Fudan University, Shanghai, China
Venue:
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
Year:
2006

Citing 12
Cited 1

Privacy-preserving data mining

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Using unknowns to prevent discovery of association rules

ACM SIGMOD Record
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Hiding Association Rules by Using Confidence and Support

IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Disclosure Limitation of Sensitive Rules

KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Privacy preserving frequent itemset mining

CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Privacy Preserving Association Rule Mining

RIDE '02 Proceedings of the 12th International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems (RIDE'02)
Protecting Sensitive Knowledge By Data Sanitization

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A Framework for High-Accuracy Privacy-Preserving Mining

ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Incognito: efficient full-domain K-anonymity

Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Maintaining data privacy in association rule mining

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Anonymizing tables

ICDT'05 Proceedings of the 10th international conference on Database Theory

Privacy-preserving frequent pattern sharing

DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent efforts have been made to address the problem of privacy preservation in data publishing. However, they mainly focus on preserving data privacy. In this paper, we address another aspect of privacy preservation in data publishing, where some of the knowledge implied by a dataset are regarded as private or sensitive information. In particular, we consider that the data are stored in a transaction database, and the knowledge is represented in the form of patterns. We present a data sanitization algorithm, called SanDB, for effectively protecting a set of sensitive patterns, meanwhile attempting to minimize the impact of data sanitization on the non-sensitive patterns. The experimental results show that SanDB can achieve significant improvement over the best approach presented in the literature.