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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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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。

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.
其他識別: U0005-2808201301233200
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