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    題名: Privacy-Preserving Classification of Data Streams
    作者: Chao, Ching-Ming;Chen, Po-Zung;Sun, Chu-Hao
    貢獻者: 淡江大學資訊工程學系
    關鍵詞: Data Streams;Data Mining;Classification;Privacy Preservation;Incremental Mining
    日期: 2009-09-01
    上傳時間: 2010-12-01 10:30:22 (UTC+8)
    出版者: 臺北縣:淡江大學
    摘要: Data mining is the information technology that extracts valuable knowledge from large amounts of data. Due to the emergence of data streams as a new type of data, data streams mining has recently become a very important and popular research issue. There have been many studies proposing efficient mining algorithms for data streams. On the other hand, data mining can cause a great threat to data privacy. Privacy-preserving data mining hence has also been studied. In this paper, we propose a method for privacy-preserving classification of data streams, called the PCDS method, which extends the process of data streams classification to achieve privacy preservation. The PCDS method is divided into two stages, which are data streams preprocessing and data streams mining, respectively. The stage of data streams preprocessing uses the data splitting and perturbation algorithm to perturb confidential data. Users can flexibly adjust the data attributes to be perturbed according to the security need. Therefore, threats and risks from releasing data can be effectively reduced. The stage of data streams mining uses the weighted average sliding window algorithm to mine perturbed data streams. When the classification error rate exceeds a predetermined threshold value, the classification model is reconstructed to maintain classification accuracy. Experimental results show that the PCDS method not only can preserve data privacy but also can mine data streams accurately.
    關聯: 淡江理工學刊=Tamkang Journal of Science and Engineering 12(3),頁321-330
    DOI: 10.6180/jase.2009.12.3.11
    顯示於類別:[資訊工程學系暨研究所] 期刊論文

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