Hiding collaborative recommendation association rules
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- 作者：Shyue-Liang Wang ; Dipen Patel ; Ayat Jafari and Tzung-Pei Hong
- 关键词：Privacy preserving data mining ; Collaborative recommendation ; Association rule
- 刊名：Applied Intelligence
- 出版年：2007
- 出版时间：August, 2007
- 年：2007
- 卷：27
- 期：1
- 页码：67-77
- 全文大小：388 KB
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Mechanical Engineering
Manufacturing, Machines and Tools - 出版者：Springer Netherlands
- ISSN：1573-7497
文摘
The concept of Privacy-Preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. For example, through data mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. There have been two types of privacy concerning data mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected. For output privacy in hiding association rules, current approaches require hidden rules or patterns to be given in advance [10, 18–21, 24, 27]. This selection of rules would require data mining process to be executed first. Based on the discovered rules and privacy requirements, hidden rules or patterns are then selected manually. However, for some applications, we are interested in hiding certain constrained classes of association rules such as collaborative recommendation association rules [15, 22]. To hide such rules, the pre-process of finding these hidden rules can be integrated into the hiding process as long as the recommended items are given. In this work, we propose two algorithms, DCIS (Decrease Confidence by Increase Support) and DCDS (Decrease Confidence by Decrease Support), to automatically hiding collaborative recommendation association rules without pre-mining and selection of hidden rules. Examples illustrating the proposed algorithms are given. Numerical simulations are performed to show the various effects of the algorithms. Recommendations of appropriate usage of the proposed algorithms based on the characteristics of databases are reported.