Preserving privacy in association rule mining with bloom filters
Qiu, Ling, Li, Yingjiu, and Wu, Xintao (2007) Preserving privacy in association rule mining with bloom filters. Journal of Intelligent Information Systems, 29 (3). pp. 253-278.
Restricted to Repository staff only
Privacy preserving association rule mining has been an active research area since recently. To this problem, there have been two different approaches—perturbation based and secure multiparty computation based. One drawback of the perturbation based approach is that it cannot always fully preserve individual’s privacy while achieving precision of mining results. The secure multiparty computation based approach works only for distributed environment and needs sophisticated protocols, which constrains its practical usage. In this paper, we propose a new approach for preserving privacy in association rule mining. The main idea is to use keyed Bloom filters to represent transactions as well as data items. The proposed approach can fully preserve privacy while maintaining the precision of mining results. The tradeoff between mining precision and storage requirement is investigated. We also propose δ-folding technique to further reduce the storage requirement without sacrificing mining precision and running time.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||association rule mining; bloom filters; privacy preserving|
|Date Deposited:||29 Jul 2009 04:00|
|FoR Codes:||08 INFORMATION AND COMPUTING SCIENCES > 0806 Information Systems > 080604 Database Management @ 100%|
|SEO Codes:||89 INFORMATION AND COMMUNICATION SERVICES > 8999 Other Information and Communication Services > 899999 Information and Communication Services not elsewhere classified @ 70%
97 EXPANDING KNOWLEDGE > 970116 Expanding Knowledge through Studies of Human Society @ 30%
|Citation Count from Web of Science||