文章基本信息

标题：CLASSIFICATION AND EVALUATION THE PRIVACY PRESERVING DISTRIBUTED DATA MINING TECHNIQUES
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作者：SOMAYYEH SEIFI MORADI ; MOHAMMAD REZA KEYVANPOUR
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2012
卷号：37
期号：2
页码：204-210
出版社：Journal of Theoretical and Applied
摘要：In recent years, the data mining techniques invarious areas have met serious challenges increasingconcernsaboutprivacy. Different techniques and algorithms have been already presented for Privacy preserving data mining (PPDM), which could be classified in two scenarios: centralized data scenario and distributed data scenario. This paper presents a Framework for classification and evaluation of the privacy preserving data mining techniques for distributed data scenario. Based on our framework the techniques are divided intothree major groups, namely Secure Multiparty Computation based techniques, Secret Sharing based techniques and Perturbation based techniques.Also in proposed framework, seven functional criteria will be used to analyze and analogically evaluation of the techniques in these three major groups. The proposed framework provides a good basis for more accuratecomparison of the given techniques to privacy preserving distributed data mining. In addition, this framework allows recognizing the overlapping amount for different approaches and identifying modern approaches in this field.
关键词：Privacy Preserving Distributed Data Mining (PPDDM); Secure Multiparty Computation (SMC); Perturbation; Secret Sharing