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關聯式規則探勘隱私保護之隱私性與可用性之評估 = Evaluating ...

國立高雄大學資訊管理學系碩士班

關聯式規則探勘隱私保護之隱私性與可用性之評估 = Evaluating Privacy and Utility in Privacy-Preserving Association Rule Mining

紀錄類型:	書目-語言資料,印刷品 : 單行本
並列題名:	Evaluating Privacy and Utility in Privacy-Preserving Association Rule Mining
作者:	宋承祐,
其他團體作者:	國立高雄大學
出版地:	高雄市
出版者:	撰者;
出版年:	2016[民105]
面頁冊數:	39面圖，表格 : 30公分;
標題:	關聯式規則探勘
標題:	Privacy-Preserving
電子資源:	http://handle.ncl.edu.tw/11296/ndltd/56930810974555846118
附註:	106年4月25日公開
附註:	參考書目: 面33-35
摘要註:	近幾年來，越多的資料被發布、被分析，而隱私保護也就越來越受重視。一些隱私的訊息，可以由資料連結、資料探勘等推論出來。k隱匿是第一個被提出來隱藏敏感資訊，不受資料的連結而洩漏隱私的概念，但卻沒有考慮到資料探勘後敏感的結果。關聯式規則隱藏的技術則是後來為了隱藏資料探勘的敏感結果而被提出。然而這些直接性的隱藏技術會有副作用，像是需要隱藏的規則卻沒被完全隱藏到、衍生了一些新規則等等。我們探討並比較先做k隱匿再做關聯式規則探勘，以及直接做關聯式規則隱藏的兩種資料保護方法的優缺點。本研究提出一個新的方法架構，來評估資料探勘後的隱私性的提升以及資料可用性的流失。比較這兩種方法的數值說明了k隱匿有著較高的隱私性的提升，而關聯式規則隱藏保留著較多的資料可用性的流失。 In recent years, privacy preservation has attracted much interest due to concerns regarding breaches of privacy when data are published and analyzed. Private information can be observed directly from published data or inferred through data mining techniques. The k-anonymity concept was first proposed to hide sensitive attribute values that could be discovered using a linking attack. Association rule hiding techniques have been proposed to hide sensitive patterns in mining results. However, these association rule hiding techniques have side effects such as hiding failure, creation of new rules, and lost rules. In addition, the k-anonymity approach does not consider hiding association rules. In this work, we extend the k-anonymity concept to hide sensitive association rules and compare it with the association rule hiding approach. We propose a novel concept of measuring privacy gain and utility loss of anonymized association rules. Numerical experiments comparing the two approaches show that the k-anonymity for association rule mining approach achieves higher privacy gain, while the direct anonymization approach of association rule hiding achieves lower utility loss. The results obtained here provide a guideline for adopting anonymization techniques under different requirements and suggests a direction for the development of new association rule hiding techniques.

關聯式規則探勘隱私保護之隱私性與可用性之評估 = Evaluating Privacy and Utility in Privacy-Preserving Association Rule Mining
宋, 承祐

關聯式規則探勘隱私保護之隱私性與可用性之評估 = Evaluating Privacy and Utility in Privacy-Preserving Association Rule Mining / 宋承祐撰 - 高雄市 : 撰者, 2016[民105]. - 39面 ; 圖，表格 ; 30公分.
106年4月25日公開參考書目: 面33-35.
關聯式規則探勘Privacy-Preserving

關聯式規則探勘隱私保護之隱私性與可用性之評估 = Evaluating Privacy and Utility in Privacy-Preserving Association Rule Mining
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