隨著電子商務化的成長，無紙化作業已經逐漸取代以往的傳統作業，資料庫的運算速度更是硬體的進步下大幅提升，在如此的背景下，資料庫的快速、即時存取早已不是難事。然而，儲存敏感性資料的資料庫，可能在有心人的操作下，利用匿名性的敏感性資料庫，對照公開性或半公開性資料庫的查詢以破解並取得具有隱私權的相關資訊，尤其往往在極具重要性，如醫療、軍事等之資料庫，造成無法彌補之損失。此外，網際網路帶來的便利化相對也伴隨網路犯罪日益增加的現象。但倘若一昧追求資料庫安全，導致非敏感性之資料無法被查詢，或是產出錯誤的資料，亦有可能導致不良副作用，例如醫生對於患者病歷資料掌握性不足而無法迅速診斷，造成搶救黃金時間的損失等情事發生。
本論文目的在研究如何在保持資料統計值的正確性之前提下，將資料庫內敏感性資料的單一辨識性移除。我們提出二種新的方法：（1）『抑制型雜訊添加機制』：結合傳統『雜訊添加』機制及『群組雜訊抑制』觀念將群組內資料值平均化，以移除其單一性；（2）『隨機型區間雜訊添加機制』：針對敏感度較高之資料值，將其添加雜訊後再取區間值來遮蔽其原始值。最後以網頁應用（Web-Based）系統來實作前述雜訊添加方式，並比較其優缺點。

With the growth of electronic commerce, paperless operations are gradually replacing traditional operations. The computing speed progresses substantially along with the advancement of hardware. Fast and instant access is not a difficult issue under this background. However, databases with sensitive data might be broken through by the intruders by combining anonymous sensitive databases with disclosure databases. If this should happen to medical and military databases and it would cause damages that can not be recovered. Besides, the convenience that Internet brings is also accompanied with the phenomenon of increasing network crimes. It is a dilemma to either give the exact data or “perturbed’ data for the users. For example, if we export the “perturbed” data, it might be happen that a doctor cannot make emergency rescue rapidly due to the low accuracy of patients’ case history.
This thesis is to remove the uniqueness of sensitive data in databases under the premise of keeping the accuracy of statistical data. We propose two new algorithms: Reducing Noise Addition and Random Noise Interval Addition. Reducing Noise Addition combines tradition noise addition and group noise reducing to generalize the data in groups and removes the uniqueness of each tuple. Random Interval Noise Addition adds noise to the random values in groups and calculates an interval to cover the original data values. At last, but not the least, we implemented our algorithms in a web-based system to analyze its feasibility.

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