Privacy-preserving DBSCAN on Horizontally Partitioned Data

摘要：

Privacy preserving data mining of distributed data is an important direction for data mining, and privacy preserving clustering is one of the main researches. At present, most privacy preserving clustering algorithms are concentrated on k-means and based on two parties and a trusted third party, clustering results are uncertain and hard to find complex shape clusters, and the protocols are inefficient because of using encryption, so we propose a algorithm called HPPDBSCAN based on semi-honest models for horizontally partitioned databases using some secure protocols such as secure sum computation, scalar product computation, standardization, and comparison by means of a semi-honest third party. The algorithm resolves the problem of privacy preserving under semihonest circumstance for multi-party. Theoretic argument and example analysis demonstrate that the scheme is secure and complete with good efficiency.

作者: JIANG Dongjie XUE Anrong JU Shiguang CHEN Weihe MA Handa

作者单位: School of Computer Science and Telecommunication Engineering,Jiangsu University,Zhenjiang,China 212013

会议类型: 国际会议

会议名称: 2008 IEEE International Symposium on IT in Medicine and Education(2008信息技术在医学和教育中的应用国际研讨会)(ITME 2008)

会议地点: 厦门

会议语种:英文

页码: 1067-1072

在线出版日期: 2008-12-12（万方平台首次上网日期，不代表论文的发表时间）