臺灣博碩士論文加值系統

雲端運算(cloud computing) 與儲存在近幾年逐漸普及，而雲端的數據儲存量也隨之增加，因此如何在加密環境下進行有效的關鍵字搜尋並與資料隱私保護結合顯
然成為重要的議題。在相關的文獻中，大多數的方法都只提供單一關鍵字和多關
鍵字的精準搜尋(exact search)，其中使用者所提出的關鍵字必須與預先定義好的
字典中的關鍵字完全符合才能進行搜尋，然而在現實生活的應用中，限制使用者
只能使用在預設字典裡所提的關鍵字的搜尋方法是非常不切實際的。目前被提出
的模糊查詢(fuzzy search) 方式，只注重在解析關鍵字結構以找出拼字錯誤而如何增強使用者下關鍵字的彈性並未被提及。本文提出了一在雲端環境下多關鍵字語
意查詢，使用者所提出的關鍵字不再受限於預設好的字典關鍵字，而是更有彈性
的提出自己所需的關鍵字，並可獲得與關鍵字最相關的文件。此外，在雲端伺服
器進行搜尋運算的同時並考量資料的隱私。透過現實資料所完成的實驗結果明確
地表現出本文所提出的搜尋方法可以有效率地達到在雲端環境下多關鍵字的語意
擴充查詢。

Cloud storages have gained popularity in the recent years. With the increasing quantity of data outsourced to cloud storages, keyword search over encrypted cloud data with the consideration of privacy preservation has become an important topic. The majority
techniques in the literature only provide exact single or multiple keyword search in which the keywords have to exactly match those in a pre-defined dictionary. However, restricting users’keywords within the pre-defined dictionary is impractical for real-world applications. Some existing fuzzy keyword search schemes only focus on dealing with spelling mistakes of keywords. The flexibility of keywords used in the search is not considered.
This paper addresses the problem of semantic multi-keyword search over encrypted cloud data. Users can use keywords not just in the pre-defined dictionary of the dataset, but with the flexibility of their own choice. The similarity of the given keywords with the search index of each document is then calculated. An adequate set of documents are selected as the results for the search based on the similarity. In addition, privacy of the search is also considered during the search executed by the third party service provider. Experiments are conducted using a dataset of massive papers in real world. The experimental analyses show that the proposed scheme can perform the semantic multi-keyword search
effectively and efficiently.

Abstract in Chinese . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Abstract in English . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
List of Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 Searchable Encryption . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Single Keyword Searchable Encryption . . . . . . . . . . . . . . . . . . 5
2.3 Multi Keyword Searchable Encryption . . . . . . . . . . . . . . . . . . . 6
2.4 Fuzzy Keyword Searchable Encryption . . . . . . . . . . . . . . . . . . 6
2.5 Some related application of Word2Vec . . . . . . . . . . . . . . . . . . . 7
3 Problem Formulation and Proposed Method . . . . . . . . . . . . . . . . . . . 8
3.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1.1 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1.2 Threat Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 Semantics-based Multi-Keyword Search over Encrypted Cloud Data (SMSE) 11
3.2.1 Document Index Generation . . . . . . . . . . . . . . . . . . . . 12
3.2.2 Semantic Search Mechanism . . . . . . . . . . . . . . . . . . . . 13
3.2.3 Evaluate word similarity - Word2Vec . . . . . . . . . . . . . . . 18
3.3 Enhanced Semantics-based Multi-Keyword Search over Encrypted Cloud
Data (E-SMSE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3.1 Document Index Generation . . . . . . . . . . . . . . . . . . . . 21
3.3.2 Semantic Search Mechanism . . . . . . . . . . . . . . . . . . . . 22
4 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.1 Dataset and Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . 26
4.2 Similarity analysis in SMSE . . . . . . . . . . . . . . . . . . . . . . . . 28
4.3 Precision analysis in E-SMSE . . . . . . . . . . . . . . . . . . . . . . . 29
4.4 Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

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