A border-based approach for hiding sensitive frequent itemsets

Sun, Xingzhi and Yu, Philip S. (2005). A border-based approach for hiding sensitive frequent itemsets. In: V. Raghavan and R. Rastogi, Fifth IEEE International Conference on Data Mining. ICDM'05: Fifth IEEE International Conference on Data Mining, Houston, Texas, (426-433). 27-30 November 2005. doi:10.1109/ICDM.2005.2


Author Sun, Xingzhi
Yu, Philip S.
Title of paper A border-based approach for hiding sensitive frequent itemsets
Conference name ICDM'05: Fifth IEEE International Conference on Data Mining
Conference location Houston, Texas
Conference dates 27-30 November 2005
Proceedings title Fifth IEEE International Conference on Data Mining   Check publisher's open access policy
Place of Publication Los Alamitos, CA, U.S.A.
Publisher IEEE Computer Society
Publication Year 2005
Sub-type Fully published paper
DOI 10.1109/ICDM.2005.2
ISBN 0-7695-2278-5
ISSN 1550-4786
Editor V. Raghavan
R. Rastogi
Start page 426
End page 433
Total pages 8
Collection year 2005
Language eng
Abstract/Summary Sharing data among organizations often leads to mutual benefit. Recent technology in data mining has enabled efficient extraction of knowledge from large databases. This, however, increases risks of disclosing the sensitive knowledge when the database is released to other parties. To address this privacy issue, one may sanitize the original database so that the sensitive knowledge is hidden. The challenge is to minimize the side effect on the quality of the sanitized database so that nonsensitive knowledge can still be mined. In this paper, we study such a problem in the context of hiding sensitive frequent itemsets by judiciously modifying the transactions in the database. To preserve the non-sensitive frequent itemsets, we propose a border-based approach to efficiently evaluate the impact of any modification to the database during the hiding process. The quality of database can be well maintained by greedily selecting the modifications with minimal side effect. Experiments results are also reported to show the effectiveness of the proposed approach. © 2005 IEEE
Subjects E1
280103 Information Storage, Retrieval and Management
700103 Information processing services
Keyword Data mining
Data privacy
Q-Index Code E1

 
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Created: Thu, 23 Aug 2007, 21:01:41 EST