隨著科技的進步與商業競爭激烈，隱私的議題比起以往也受到更多的重視和注意。在資料探勘中，擷取有意義的關聯法則是一個很重要的技術；相對的，在探勘過程中，這個技術也可能造成一些隱私問題。因此，為了要避免敏感資訊被揭露導致隱私問題，有許多研究開始將敏感的關聯法則隱藏。不過，科技與網路的快速發展，使資料持續不斷的增加，並且敏感關聯法則也可能會跟隨的時間與政策有所變化，在保護敏感關聯法則上形成挑戰。就我們所知，現存的研究中，隱藏敏感關聯式法則的技術中還不能有效的處理動態的資料與敏感法則。

為了解決這些問題，我們提出一個在漸進式資料中保護動態敏感關聯法則的架構HSAi 和 HDSA。其中HSAi是在漸進的資料下保護敏感關聯法則，而為了隱藏敏感性法則，我們設計有效的策略去選擇適當的犧牲項目與交易進行刪除。而HDSA則是保護動態的敏感關聯法則演算法，動態包含增加與刪除兩部份，其中刪除敏感關聯法則，則是將已隱藏的關聯法則盡可能的再次出現於探勘結果中。這兩個演算法除了保護敏感關聯法則的目標，還希望可以使釋出的資料集能產生的副作用盡可能的小，盡可能的不影響非敏感關聯法則。而實驗結果也顯示，此架構可以釋出品質較高的資料集。

As the advancement of technologies as well as the intense competition of business, the issues of privacy have acquiring more attention. Mining association rule is the significant technique in data mining. However, it may cause some privacy problem in mining processes. Many researches, thus, start to hide sensitive association rules due to avoid the sensitive information exposed. However, the development of computers and Internet technologies is so fast that data are increasing successively. In addition, sensitive association rules will change with time and policy. These both are the challenges for protecting sensitive association rules. Most exist technologies of hiding sensitive association rules cannot handle dynamic data and sensitive rules effectively.

For solving these problems, this paper proposed a framework to protect dynamic sensitive association rules in incremental environment, HSAi and HDSA. HSAi is the algorithm to protect sensitive association rule in incremental data and we design the strategy to select appropriate victim transactions and items to delete them in order to hide sensitive association rules. HDSA is the algorithm for protecting dynamic sensitive rules, including adding and deleting. The mean of the deleting sensitive rule is the association rule that hidden can show again in the mining result. The goals of HSAi and HDSA are not only protecting sensitive rules but also producing least side effect from released dataset. Experiment results represent that the framework situation of incremental data and dynamic sensitive rules both can cause least side effects and maintain a desirable quality of sanitized database as well.

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