臺灣博碩士論文加值系統

微聚合(microaggregation)滿足k-匿名，是避免個體隱私洩漏的統計洩漏控制技術。微聚合技術處理後的資料集，每一筆資料至少會和其他k-1筆資料完全相同，藉此達到隱藏個人身分的目的。由於微聚合處理會造成資訊量的損失，因此如何有效降低微聚合處理所導致的資訊損失，確保處理後資料集仍具有分析研究的可用性，一直是過去研究的重點。而本研究提出兩階段密度為基的微聚合演算法HDF，以降低微聚合處理的資訊損失，實驗結果顯示，此演算法較過去的微聚合演算法在更多的情況下有較低的資訊損失量。接著本研究使用微聚合處理後之資料集建立分類器，以分類準確率當作資料之可用性，實驗結果顯示，經HDF演算法處理後之資料集有不錯的可用性。

Microaggregation, which satisfies k-anonymity is a statistical disclosure control technique that has been widely used to avoid disclosure of respondent privacy. In order to protect individual privacy, a record must be identical to at least k-1 other record after using the technology of microaggregation. However, the protection provided by microaggregation also entails information loss. Therefore, recent research has focused on how to minimize the information loss. To lower information loss, this study proposes a two-phase density-based microaggregation method, called HDF. The experiments show that HDF algorithm gets the lower information loss than other algorithms in literatures in most of the test conditions.

書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目錄 vii
表目錄 ix
圖目錄 x
第一章 序論 1
1.1 研究背景 1
1.2 研究目的 1
1.3 論文架構 2
第二章 文獻探討 3
2.1 微聚合 3
2.1.1 微聚合概念 3
2.1.2 資訊損失 4
2.1.3 最佳化微聚合 5
2.2 微聚合文獻回顧 6
2.2.1 微聚合面向 6
2.2.2 微聚合演算法 7
第三章 研究方法 12
3.1 Density-Based Algorithm 12
3.1.1 密度之概念 12
3.1.2 HDF Algorithm 13
3.1.3 LDF Algorithm 15
3.2 MDAV-2 17
第四章 實驗結果 18
4.1 資訊損失量實驗 18
4.1.1 統計洩漏控制資料集 18
4.1.2 實驗規劃及結果 18
4.2 資料可用性實驗 23
4.2.1 UCI資料集 23
4.2.2 封閉式實驗設計與結果 24
4.2.3開放式實驗設計與結果 29
第五章 結論與未來展望 32
參考文獻 33

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