Privacy-Preserving Distributed Association Rule Mining Based on the Secret Sharing Technique

摘要：

Due to privacy law and motivation of business interests, privacy is concerned and has become an important issue in data mining. This paper explores the issue of privacy-preserving distributed association rule mining in vertically partitioned data among multiple parties, and proposes a collusion-resistant algorithm of distributed association rule mining based on the Shamirs secret sharing technique, which prevents effectively the collusive behaviors and conducts the computations across the parties without compromising their data privacy. Additionally, analyses with regard to the security,efficiency and correctness of the proposed algorithm are given.

关键词： association rule mining privacy security secret sharing.

作者: Xinjing Ge Li Yan Jianming Zhu Wenjie Shi

作者单位: School of Information. Central University of Finance and Economics Beijing, 100081, China School of Information. Renmin University of China.Beijing, 100872. China School of Information. Central University of Finance and Economics.Beijing, 100081. China School of Foreign Studies. Central University of Finance and Economics.Beijing, 100081. China

会议类型: 国际会议

会议名称: The 2nd International Conference on Software Engineering and Data Mining(IEEE 第二届国际软件工程和数据挖掘学术大会 SEDM 2010)

会议地点: 成都

会议语种:英文

页码: 274-279

在线出版日期: 2010-06-23（万方平台首次上网日期，不代表论文的发表时间）