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研究生:許柏強
研究生(外文):Po-Chiang Hsu
論文名稱:新利潤探勘的隱私保護演算法
論文名稱(外文):Novel Algorithms for Privacy Preserving Utility Mining
指導教授:葉介山葉介山引用關係
指導教授(外文):Jieh-Shan Yeh
學位類別:碩士
校院名稱:靜宜大學
系所名稱:資訊管理學系研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007/07/
畢業學年度:95
語文別:英文
論文頁數:38
中文關鍵詞:隱私保護資料探勘利潤探勘敏感項目集
外文關鍵詞:Privacy Preserving Data MiningUtility miningSensitive itemsets
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隱私保護資料探勘是一個受歡迎的研究方向,但到目前為止,在利潤探勘的隱私保護上尚未被討論。如何在分享的過程中取得隱私保護與知識發掘間的平衡是一個重要的議題。本研究專注在利潤探勘的隱私保護並且提出HHUIF及MSICF兩個有效率的演算法來達成隱藏敏感項目集的目標,使得競爭對手不能從修改過的資料庫中探勘出敏感項目集。除此之外,我們在隱藏敏感項目集的過程中盡可能將處理過的資料庫影響降至最低。實驗結果顯示在兩個人造的資料集中,HHUIF演算法相較於MSICF演算法有較低的意外隱藏,此外,MSICF演算法相較於HHUIF演算法在原始資料庫與處理後資料庫間總是有較低的差異除了在MinUtility = 4000的情況下。
Privacy Preserving Data Mining (PPDM) is a popular research direction in data mining, but so far privacy preserving utility mining was not discussed. How to strike a balance between privacy protection and knowledge discovery in the sharing process is an important issue. This study focuses on the privacy preserving utility mining and present two effective algorithms, HHUIF and MSICF, to achieve the goal of hiding sensitive itemsets so that the adversaries can not mine them from the modified database. In addition, we minimize the impact on the sanitized database in the process of hiding sensitive itemsets. In our experimental results, HHUIF has the lower miss costs than MSICF does in two synthetic datasets. Besides, MSICF has the lower difference between the original and sanitized databases than HHUIF has, except in the case where MinUtility = 4000.
Contents
Abstract i
Chinese Abstract ii
Acknowledgements iii
Contents iv
List of Tables vi
List of Figures vii
Chapter 1 Introduction 1
Chapter 2 Related Works 4
2.1 Association Rule Mining 4
2.2 Utility Mining 8
2.3 Privacy Preserving of Association Rule Mining 11
2.4 The Sanitization Process 15
Chapter 3 Proposed Algorithms 16
3.1 The General Approach 16
3.2 The Sanitization Algorithms 17
3.2.1 Hiding High Utility Item First Algorithm 17
3.2.2 Maximum Sensitive Itemsets Conflict First Algorithm 21
Chapter 4 Experimental Results 24
4.1 IBM Synthetic Data Generator 24
4.2 Datasets 25
4.3 Effectiveness Measurement 25
4.4 Comparison of Hiding Failure 27
4.5 Comparison of Miss Cost 28
4.6 Comparison of Difference between the Original and Sanitized Databases 32
Chapter 5 Conclusions and Future Work 34
Reference 35
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