Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/9031
標題: KAMP:樣式組合之k-匿名化保護
KAMP:Preserving k-anonymity for Combinations of Patterns
作者: 許家豪
Hsu, Chia-Hao
關鍵字: k-匿名化
k-anonymity
隱私保護
Re-identification by linking攻擊
Privacy preserving
Re-identification by linking attacks
出版社: 電機工程學系所
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摘要: 現今,因企業或組織所生成及累積的資料越具龐大,將資料委外儲存、管理或進行知識挖掘已成為一種典範。然而,這些資料雖蘊含著有價值的知識,但同時亦潛藏著大量敏感資訊,極可能成為個人隱私上的一大隱憂。為避免發佈共享或委外之資料遭受交叉辨識(Re-identification by linking)攻擊而導致個人身份曝光,因此資料在發佈之前必須先經過隱私保護處理。 不同於先前研究著重在個別項目或樣式之k-匿名化處理,我們提出多樣式之k-匿名化問題,防止以多個樣式組合所進行的交叉辨識攻擊,並且提出KAMP-p1演算法,對資料進行概括化及隱匿化之隱私保護處理。為顯示本研究演算法之有效性,我們各別在真實資料集及人造資料集進行實驗,實驗結果顯示KAMP-p1演算法可有效的達到k-匿名化之效果,同時保留了原始資料的大部分資訊。
As huge data are increasingly generated and accumulated, outsourcing data for storage, management, and knowledge discovery is becoming a paradigm. However, given that a lot of sensitive information and valuable knowledge are hidden in data, the outsourcing of data is vulnerable to privacy crises and leads to demands for generalization or suppression techniques to protect data from re-identification attacks. Differing from previous works that aim at satisfying the k-anonymity on individual patterns or individual items, we propose the k-anonymity of multi-pattern (KAMP) problem to protect data from re-identifying users by using a combination of patterns and also propose the KAMP-p1 algorithm to generalize and suppress data. To study the effectiveness of the proposed algorithm, we conduct experiments on a synthetic and a small real dataset. The experimental results show that KAMP-p1 algorithm can satisfy k-anonymity while preserving many patterns in order to retain useful knowledge for decision making.
URI: http://hdl.handle.net/11455/9031
其他識別: U0005-2808201301233200
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2808201301233200
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