Latent Dirichlet Conditional Naive-Bayes Models for Privacy-Preservation Clustering

摘要：

The paper introduces a model for privacy preservation clustering which can handle the problems of privacy preser-vation,distributed computing. First, the latent variables in Latent Dirichlet Conditional Naive-Bayes Models(LD-CNB) are redefined and some terminologies are defined. Second, Variational approximation inference for LD-CNB is stated in detail. Third, base on the variational approximation inference, we design a distributed EM algorithm for privacy preservation clustering. Finally, some datasets from UCI are chosen for experiment, Compared with the distributed k-means algorithm, the results show LD-CNB algorithm does work better and LD-CNB can work distributed, so LD-CNB can protect privacy information.

作者: Hongjun Wang Zhishu Li Liping Liu Yang Cheng

作者单位: School of Computer Science,Sichuan University,610054 Chengdu,China Chengdu Textile College, 610000 Chengdu,China

会议类型: 国际会议

会议名称: The International Conference on Communication Software and Networks(2009 IEEE通信软件与网络国际会议 ICCSN 2009)

会议地点: 成都

会议语种:英文

页码: 684-688

在线出版日期: 2009-02-20（万方平台首次上网日期，不代表论文的发表时间）