近年來，雲端儲存服務的使用越來越廣泛。憑藉較低的設備成本和高容量的優勢，一些企業和使用者傾向於將他們的數據資料從本地的儲存裝置移動到遠程的儲存裝置上，例如雲端伺服器。為了使用者讓使用者能在雲端伺服器上有效率的搜尋儲存的數據資料，透過關鍵字的搜尋方法是現在廣為使用的搜尋方法。而隨著資訊安全意識的抬頭，數據的擁有者希望放在雲端伺服器中的資料能保有隱私不被不信任的使用者窺探，同時使用者也希望自身的查詢內容不會被不被信任的伺服器紀錄，因此將數據和查詢加密是最有效利用方式。然而加密過後的密文已經失去明文所具有的關係，這會造成在關鍵詞搜尋上增加許多困難度，
此外大部分的搜尋方法大多無法有效率的從使用者所下的關鍵字中獲取使用者真正感興趣的資料。為了解決這些問題，本研究提出一種基於詞嵌入(Word Embedding)的語意搜尋演算法。其中詞嵌入的模型是由神經網路(Neural Network)模型的計算來實現，神經網路模型可以學習語意資料庫(corpus)中詞與詞之間的語意關係，並以向量來表示單詞。透過使用詞嵌入的模型，生成文檔索引向量(document index vector)和查詢向量(query vector)。最後本論文提出的方案可以將查詢向量和索引向量加密為密文，在保護用戶的隱私和文檔的安全性時同時能保有搜尋的效率。

The services of cloud storage have been very popular in recent years. With the superiority of low-cost and high-capacity, people are inclined to move their data from a local computer to a remote facility such as the cloud server. The majority of the existing methods for searching data on the cloud concentrate on keyword-based search scheme. With the rise of information security awareness, data owners hope that the data placed in the cloud server can keep privacy from being snooped by untrusted users, and users also hope that their query content will not be record by untrusted server. Therefore, encrypting data and queries is the most common way.However, the encrypted ciphertext has lost the relationship of the original plaintext, which will cause many difficulties in keyword search.In addition, most of the existing search methods are not able to efficiently obtain the information that the user is really interested in from the user's query keywords. To address these problems, this study proposes a word embedding based semantic search scheme for searching documents on the cloud. The word embedding model is implemented by a neural network. The neural network model can learn the semantic relationship between words in the corpus and express the words in vectors. By using a word-embedded model, a document index vector and a query vector can be generated. The proposed scheme can encrypt the query vector and the index vector into ciphertext, which can preserve the efficiency of the search while protecting the privacy of the user and the security of the document.

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