敏感連結之屬性去關聯性隱私保護

ADSL: Attribute Disassociation on Sensitive Linkages

李孟霖(Meng-Lin Lee)

指導教授 ： 鄭伯炤

國立中正大學/工學院/通訊工程學系/碩士(2013年)

摘要

隨著資訊與通訊科技(Information and Communications Technology)的蓬勃發展，越來越多服務透過電子化資料的方式來提高服務效率和品質，這些資料中包含了許多可以直接或間接辨識出資料對象本身的隱私資訊，隨著個人保護隱私的意識高漲和法律規範，資料提供者在使用或共享資料前都必須先進行去識別化(De-identification)處理讓資料達到k-匿名(k-Anonymity)隱私規範來防止隱私揭露(Disclosure)發生。
隨著個人保護隱私的意識高漲與法律規範之制定，資料提供者在使用或共享資料前都必須先進行去識別化(De-identification)處理以求資料達到隱私規範得以防止隱私揭露(Disclosure)發生。近年來，少數研究者已意識到k-匿名並無法完全預防隱私揭露。為解決這樣的隱私威脅，去關聯性是其中一種作法，必須在發佈的資料中保持個人資料之隱私保護。本研究將以新的觀點來防止隱私揭露，正視資料隱私保護。
本研究使用簡易的關聯性分析方式，提出新穎且有效的敏感連結之屬性去關聯性隱私保護－「ADSL」。最後經實驗結果來呈現ADSL能達到去除關聯性，同時在關鍵屬性的資料維持較高真實性。

關鍵字

去關聯性 ； 隱私保護 ； k-匿名

並列摘要

k-Anonymity and its variants provide primitive privacy protections for re-identifying any individual in a given database. However, they are still vulnerable to re-identification during correlating attribute associations. To prevent confidential information disclosure for specific association rule mining, we propose an attribute disassociation de-identification method called ADSL to mask the data before releasing to the public.
The experiments show that the proposed method not only satisfies the privacy protection requirement but also shows effectiveness of the proposed method in association rule mining.

並列關鍵字

Association rule mining ； Privacy-preserving ； k-anonymity

參考文獻

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