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研究生:莊筑君
研究生(外文):Chuang Chu-Chu
論文名稱:發展隱私保護技術於案例式推理系統之研究
論文名稱(外文):A Novel Approach for Privacy Preserving in Case-based Reasoning
指導教授:劉正祥劉正祥引用關係
指導教授(外文):Liu Cheng-Hsiang
學位類別:碩士
校院名稱:國立屏東科技大學
系所名稱:工業管理系所
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:85
中文關鍵詞:隱私保護資料探勘技術數值預測資料擾動
外文關鍵詞:Privacy preserving data miningNumeric predictionData perturbation
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隨著大數據時代的來臨,隱私保護的議題逐漸受到重視,在資料處理的過程中,如何防止重要資料洩漏是相當重要的。目前已有許多研究針對資料隱私保護提出不同的隱私保護資料探勘技術(Privacy preserving data mining, PPDM),但是方法都過於複雜繁瑣。因此,本研究利用簡單的統計方法發展出一套適應性資料擾動技術(Adaptive Data Perturbation Technique, ADPT)應用於Z-CBR系統做數值預測。本研究運用Z-CBR系統內的Z分數索引方法(Z-indexing)建構分類樹,透過此分類樹將案例庫分為數個具有相似特性的資料集合,並使用ADPT方法將該資料集合內的資料進行擾動,產出一組具隱私保護效用的合成資料。最後,利用UCI(University of California-Irvine) 內的五個資料集合進行實驗,將比較實驗結果在資料擾動前與擾動後的變異程度及計算數值預測的準確性。
In the big data era, privacy protection is a major issue. Preventing data leaks during processing is essential. Although many different Privacy Preserving Data Mining methods have been proposed, they have all proven to be too complicated. Therefore, this study applied simple statistics to develop an Adaptive Data Perturbation Technique (ADPT) for numeric prediction in a Z-CBR system. The classification tree was built by using Z-indexing in the Z-CBR system. The classification tree was used to divide the case base into several data clusters with similar features. The data in the cluster were then perturbed by ADPT to generate compound data with privacy protection. Finally, an experiment was performed using five data clusters from University of California, Irvine. Experimental results, including data forecasting accuracy, were compared before and after data perturbation.
摘要
Abstract
目錄
圖目錄
表目錄
壹、緒論
1.1研究背景與動機
1.2研究目的
1.3研究範圍與限制
1.4研究架構與流程
貳、文獻探討
2.1隱私保護資料探勘技術
2.2案例式推理
2.3Z-CBR方法
參、研究方法
肆、實驗設計
伍、實驗結果
陸、結論
參考文獻
附錄
作者簡介

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2.陳宏吉,案例式推理系統應用於數值預測之研究,碩士論文,屏東科技大學工業管理系,民國100年。
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