現今資料探勘的技術受到很大的重視，其中高頻項目集和關聯法則的探勘可以應用在很多不同的領域上，但是隨著技術越來越進步，所帶來隱私的議題也越來越受到重視。在過去保護交易資料庫內敏感資訊的技術上，都是以單一的最小支持度或是隱私係數來作為保護的基準，並且從中取得機密資訊保護和非機密資訊保存上得平衡點。但是在實際生活中，不同的項目集因為特性上得不同，可能需要運用不同的最小支持度去判斷他是否為頻繁的，因此在本論文中，我們預期加入考慮多門檻值，以及在多門檻值下頻繁項目集以及關聯法則的特性，來提出兩個新的架構TBF和GTBA來保護機密的資訊，使得對敏感資訊的保護更切合於它本身的特性並且藉此在修改過後的資料庫內保留更多的非機密資訊。另外加入一些機制對效率、擴充性和對資料庫最小影響方面作最佳化。在實驗的部份，本論文提出在多門檻值下對淨除演算法的新的評估公式，並且選用不同特性的資料集，以及不同特性的機密資訊去評估和過去相關保護方法在單一門檻值、隱私係數、以及多重門檻值下作比較，GTBA和TBF都能夠在保護隱私的情況下，保存更多原有非機密的資訊。在效率上也有不錯的表現。

Frequent pattern and association rule mining are popular topics in data mining. With the advance of these techniques, privacy issues attract more and more attentions in recent years. In this field, previous works hide sensitive information based on a uniform support threshold or using a privacy disclosure threshold. However in practical applications, we probably need to apply different support thresholds on different itemsets to reflect their significance. In this study, two sanitization frameworks, TBF and GTBA, are proposed to protect sensitive patterns and sensitive association rules with the concept of non-uniform sensitive thresholds, respectively. Based on different sensitive thresholds, the hiding result is closer to user requirements and real applications. Besides, the strategy of choosing rules, victim items, and victim transactions are proposed to preserve more information of the original dataset. In addition, we apply hash table, inverted file and some index skills to speed up the whole sanitization process. As shown in the experimental results, our frameworks can protect sensitive knowledge well not only under multiple thresholds but also a uniform threshold. Furthermore, these frameworks preserve more information, and reach higher similarity between the original dataset and the modified dataset. Moreover, the efficiency and the scalability can also be maintained.

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