由於雲計算的日益普及與資訊安全意識抬頭，數據擁有者希望所上傳之數據能夠保有隱私不被他人窺探，將數據加密並且可以直接進行關鍵字搜尋是最有效利用的理想技術。然而，在加密數據之後卻會降低這些數據的可利用性，由於加密過後的密文已經失去明文所具有的關係，造成在關鍵詞搜尋上增加許多困難度，目前針對在密文上進行查找的問題稱為可搜尋加密，但過去的研究為每個文檔使用一個較長的索引，以便於在大型數據集中搜索關鍵字，向量中的每個位元各代表一個關鍵字，為了提供更多的關鍵字以利查詢，向量的長度便顯得冗長，擴大占用儲存空間，並且導致計算量急劇增加。在本論文中，我們的目的在減少索引向量的大小以提升查詢效率，透過潛在語意分析方法來分析數據，發掘不同字詞之間的關聯，選擇主要維度來保留原始數據中的重要訊息，將數據從高維空間轉換為低維空間以便建立更具效率的查詢索引向量。本論文提出之方法同時解決長索引問題和安全搜索加密數據問題。以實際資料集實驗模擬，顯示雲數據搜索的時間效率的改進。

Now with the growing popularity of cloud computing, data owners outsource the data to cloud for convenience management. Despite the advantages of cloud storage, putting a lot of attention on data security is definitely necessary. Before outsourcing the sensitive data, data owners should encrypt it for privacy requirements. However, the encryption could make search problem of utilization become difficult by keywords. There are some existing techniques provide keyword search over encrypted data. Those strategies contain weakness about the inefficient searchable vectors. The previous strategies maintain a large number of words as the elements to construct the index for each document. It will cause the amount of calculation that sharply increase. In this thesis, we propose a novel keyword search over encrypted data scheme by reducing the size of the index vectors. First, we analyze data through latent semantic analysis, to discover the association between different words. Then, we select the principal dimension to retain the important information of dataset. After transforming the data from high-dimensional space to low-dimensional space, we provide appropriate result to the user's request. Our proposed scheme support approximate keyword search with premeditate data analysis rather than maintain large number of words. Experiments on real-world dataset show that our scheme not only provides flexibly query for users, but also effectively reduce the amount of calculation.

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