Privacy-Preserving Clustering of Data Streams
學年 99
學期 1
出版(發表)日期 2010-09-01
作品名稱 Privacy-Preserving Clustering of Data Streams
著者 Chao, Ching-Ming; Chen, Po-Zung; Sun, Chu-Hao
單位 淡江大學資訊工程學系
出版者 臺北縣:淡江大學
著錄名稱、卷期、頁數 淡江理工學刊=Tamkang Journal of Science and Engineering 13(3),頁349-358
摘要 As most previous studies on privacy-preserving data mining placed specific importance on the security of massive amounts of data from a static database, consequently data undergoing privacy-preservation often leads to a decline in the accuracy of mining results. Furthermore, following by the rapid advancement of Internet and telecommunication technology, subsequently data types have transformed from traditional static data into data streams with consecutive, rapid, temporal, and unpredictable properties. Due to the increase of such data types, traditional privacy-preserving data mining algorithms requiring complex calculation are no longer applicable.
 As a result, this paper has proposed a method of Privacy-Preserving Clustering of Data Streams (PPCDS) to improve data stream mining procedures while concurrently preserving privacy with a high degree of mining accuracy. PPCDS is mainly composed of two phases: Rotation-Based Perturbation and cluster mining. In the phase of data rotating perturbation phase, a rotation transformation matrix is applied to rapidly perturb the data streams in order to preserve data privacy. In the cluster mining phase, perturbed data will first establish a micro-cluster through optimization of cluster centers, then applying statistical calculation to update a micro-cluster, as well as using geometric time frame to allocate and store a micro-cluster, and finally output mining result through a macro-cluster generation. Two simple data structure are added in the macro-cluster generation process to avoid recalculating the distance between the macro-point and the cluster center in the generation process. This process reduces the repeated calculation time in order to enhance mining efficiency without losing mining accuracy.
關鍵字 Privacy-Preserving; Data Mining; Data Stream; Clustering
語言 en
ISSN 1560-6686
期刊性質 國際
收錄於 EI
國別 TWN
出版型式 紙本

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