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研究生:徐韻雯
研究生(外文):Yun-Wen Hsu
論文名稱:具隱私保護之醫療健檢關聯規則探勘研究
論文名稱(外文):A Study of Privacy Preserving Association Rule Mining in Health Examination
指導教授:薛夙珍薛夙珍引用關係
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊管理系碩士班
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
畢業學年度:95
語文別:中文
論文頁數:80
中文關鍵詞:阻擋技術醫療健檢隱私保護關聯規則探勘群集分析
外文關鍵詞:Blocking TechniqueHealth ExaminationPrivacy PreservingAssociation Rule MiningClustering
相關次數:
  • 被引用被引用:3
  • 點閱點閱:407
  • 評分評分:
  • 下載下載:7
  • 收藏至我的研究室書目清單書目收藏:1
隨著資訊科技的快速發展,醫療資料都已經以資訊化儲存,在醫療資訊系統的廣泛使用下,資料庫中的資料量亦隨之快速的累積,蘊藏在其中的資訊也相對的提高,而最常被用來分析資料的技術即為資料探勘,資料探勘技術能從大量的醫療資料中找出潛在有價值的資訊,以幫助醫療人員決策制定。然而,資料探勘的技術已有許多應用於醫療領域中,但資料探勘的過程中卻也增加了侵犯資料隱私的威脅。目前資料隱私愈受重視,如何防止資料探勘過程中隱密性資料或資訊被揭露,達到具隱私防護之資料探勘,將是一個重要的探討議題。
本論文主要針對醫療資料進行關聯規則探勘過程中加入隱私保護技術,從醫療健檢資源限制及資訊分享兩個觀點進行探討,分別提出隱私保護之醫療健檢敏感性項目集及隱私保護之群聚化醫療健檢敏感性關聯規則。隱私保護醫療健檢敏感性項目集方法設計上,基於阻擋技術提出一種未知值取代方式,此方法利用常態分佈的特性均勻取代敏感項目集,以保護敏感性資料不被揭露,再者,群聚化醫療健檢資訊保護之方法設計上,則著重於如何隱藏敏感性規則,藉由分群技術及關聯規則隱私保護技術,使釋出的資料庫無法推論敏感性關聯規則,以達到隱藏資訊的目的。
Medical data has being collected in many databases as the advance of the information technology. Many techniques are applied in analyzing these data for decision support in potential medical applications. Among these techniques, data mining has emerged since much valuable knowledge is hidden in the volume medical treatment data. Nevertheless, the mining process may cause threats to the invasion of privacy. Privacy-preserving data mining, which finds out knowledge but preserves the privacy, consequently is becoming an important issue.
In this thesis, privacy-preserving mechanisms for association rule mining are investigated. A mechanism which employs uniform replacement for privacy protection of health examination data in a resource restricted medical environment is proposed. The proposed approach transforms the original data into a masked form by replacing items in sensitive item-sets with question-marks, with the consideration of normal distributions. In addition, the sharing and mining of health examination data is studied. A mechanism which employs clustering analysis and privacy preserving association rule mining techniques is used. The proposed mechanisms assure that the original sensitive rules will not be inferred from release of the database.
摘 要 I
Abstract II
誌 謝 III
目 錄 IV
圖目錄 XI
表目錄 XIII
第一章、緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
1.4 研究範圍 4
1.5 研究步驟 6
1.6 論文架構 8
第二章、文獻探討 10
2.1 資料探勘 10
2.2 關聯規則 13
2.2.1 Apriori 演算法 14
2.3 量化關聯規則 17
2.4 群聚分析 20
2.5 醫療資料探勘相關研究與應用 23
2.6 隱私保護資料探勘 27
2.7 隱私保護之關聯規則探勘 31
2.8 隱私保護關聯規則探勘評估 42
2.9 小結 44
第三章、具隱私保護醫療健檢關聯規則探勘 45
3.1 方法架構 45
3.2 方法介紹 47
3.3 範例說明 49
3.4 實驗分析 55
3.4.1 實驗環境 55
3.4.2 實驗資料 55
3.4.3 評估方法 55
3.4.4 實驗分析與設計 56
3.5 小結 59
第四章、具隱私保護之群聚化醫療健檢關聯規則 60
4.1 方法架構 60
4.2 範例說明 63
4.3 實驗結果 68
4.4 分析與討論 70
4.5 小結 71
第五章、結論 72
5.1 研究貢獻 72
5.2 未來研究方向 73
參考文獻 74
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