【摘要】：隨著電子商務(wù)、社交網(wǎng)絡(luò)、企業(yè)信息化的快速發(fā)展,個人及企業(yè)產(chǎn)生的數(shù)據(jù)量越來越多,而云存儲的興起使得企業(yè)和個人將大量數(shù)據(jù)上傳至云端,為了保護其中的用戶的敏感數(shù)據(jù)不被泄露,可行的做法是將數(shù)據(jù)外包至云服務(wù)器之前對數(shù)據(jù)先進行加密處理。如何利用密文搜索技術(shù)對加密后的數(shù)據(jù)進行查詢就是隱私查詢保護技術(shù)研究的問題。在目前的研究成果中,一些學(xué)者通過設(shè)計優(yōu)秀的索引及加密算法實現(xiàn)了從單個關(guān)鍵詞到多關(guān)鍵詞檢索的突破,處理效率也得到較大的提升。然而,這些已有的模式大多數(shù)只能進行精確匹配,一旦在用戶的查詢關(guān)鍵詞中出現(xiàn)拼寫錯誤,云服務(wù)器返回的結(jié)果可能不是用戶想要得到的查詢結(jié)果,甚至返回結(jié)果可能為空。因此,基于密文的模糊搜索成為了一個新的挑戰(zhàn)問題。在最近的研究成果中,已經(jīng)有部分模式開始支持密文的模糊搜索,但是這些模式幾乎都需要構(gòu)建關(guān)鍵詞詞典,導(dǎo)致查詢效率不夠理想。基于此,本文提出了一種新的多關(guān)鍵詞模糊搜索方案,該方案同時支持中英文關(guān)鍵詞的模糊搜索。本文具體研究工作如下：1.研究了當(dāng)前的密文可搜索技術(shù),針對基于關(guān)鍵詞字典的密文模糊搜索模式,通過分析其查詢效率的不足及原因,提出一種新的搜索模式,即采用局部敏感哈希技術(shù)和布隆過濾器技術(shù)來處理模糊搜索,從而順利構(gòu)建模糊搜索模式。2.在前面提出的模式基礎(chǔ)之上,進一步提出了針對中文的模糊搜索模式,該模式不僅支持用戶輸入英文查詢,亦可輸入中文查詢。此外,本文還設(shè)計了查詢結(jié)果排序機制,根據(jù)用戶輸入的關(guān)鍵詞,采用自定義加權(quán)方法對查詢結(jié)果排序。該排序機制使得模式能夠返回有序的結(jié)果列表,位于前列的記錄即是與用戶查詢內(nèi)容最相關(guān)的。3.為了測試本文提出的模式的可行性和搜索效率。針對中、英文的模糊搜索模式,在選取的數(shù)據(jù)源上進行反復(fù)實驗,驗證了各模式的可行性、高效性、安全性及較高的搜索準(zhǔn)確性。實驗結(jié)果還表明返回給用戶的結(jié)果集的排列順序的準(zhǔn)確率較高,滿足用戶的查詢期望。
[Abstract]:With the rapid development of electronic commerce, social network and enterprise information, the amount of data generated by individuals and enterprises is increasing, and the rise of cloud storage makes enterprises and individuals upload a lot of data to the cloud. In order to protect the sensitive data of the user from being leaked, it is feasible to encrypt the data before outsourcing it to the cloud server. How to use ciphertext search technology to query encrypted data is the problem of privacy query protection technology. In the current research results, some scholars through the design of excellent indexing and encryption algorithms to achieve a breakthrough from a single keyword to multiple keyword retrieval, processing efficiency has also been greatly improved. However, most of these existing patterns can only be accurately matched. Once a spelling error occurs in the user's query keywords, the result returned by the cloud server may not be the query result that the user wants, or even the result may be empty. Therefore, the fuzzy search based on ciphertext becomes a new challenge. In recent research results, some patterns have begun to support the fuzzy search of ciphertext, but almost all of these patterns need to build keyword dictionaries, resulting in poor query efficiency. Based on this, a new multi-keyword fuzzy search scheme is proposed, which supports both Chinese and English keyword fuzzy search. The specific research work of this paper is as follows: 1. In this paper, the current searchable ciphertext technology is studied. According to the fuzzy search mode of ciphertext based on keyword dictionary, a new search mode is proposed by analyzing the inadequacies and reasons of its query efficiency. That is to say, local sensitive hashing technique and Bron filter technique are used to deal with fuzzy search, so that the fuzzy search pattern can be constructed smoothly. 2. Based on the proposed model, a fuzzy search model for Chinese is proposed, which not only supports users to input English queries, but also inputs Chinese queries. In addition, a query result sorting mechanism is designed in this paper. According to the keywords entered by the user, the query results are sorted by a custom weighting method. The sorting mechanism enables the schema to return an ordered list of results, with the front-row records being the most relevant. 3. 3 to the content of the user query. In order to test the feasibility of the proposed model and search efficiency. In view of the fuzzy search patterns in Chinese and English, repeated experiments are carried out on the selected data sources to verify the feasibility, efficiency, security and high search accuracy of each model. The experimental results also show that the accuracy of the order of the result set returned to the user is high, which meets the query expectation of the user.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP309

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