本文選題：云存儲 + 數(shù)據(jù)驗證��； 參考：《浙江大學》2013年碩士論文

【摘要】：隨著云計算的廣泛應用,越來越多的用戶將自己的數(shù)據(jù)和文件存儲在云服務器上。云存儲在為用戶帶來便捷的同時,也帶來一些問題。用戶的數(shù)據(jù)和文件在服務器上可能會出現(xiàn)意外的篡改或丟失,而出現(xiàn)這種情況的時候用戶和云服務器可能都發(fā)現(xiàn)不了。因此,需要定期對用戶的數(shù)據(jù)進行驗證來保證用戶的數(shù)據(jù)和文件的完整性和正確性。 本文首先對云存儲數(shù)據(jù)驗證的方法做了綜述,介紹了數(shù)據(jù)驗證的概念和驗證方法的設計原則,并簡要介紹了現(xiàn)有的方法。 然后針對現(xiàn)有方法中存在的不足,提出了自己的改進思路和方法�，F(xiàn)有驗證方法,每次驗證的計算過程只能對一個用戶的數(shù)據(jù)進行驗證,而本文提出一種方法,可以在一次驗證的計算過程中,對多個用戶的數(shù)據(jù)進行合并驗證,不僅節(jié)省了驗證的次數(shù),更減少了驗證過程中驗證方與云存儲方之間的數(shù)據(jù)傳輸量。然后針對用戶的數(shù)據(jù)會動態(tài)變化的問題,已有的驗證方法用一種叫做梅克爾哈希樹(Merkle Hash Tree)的哈希樹來維護文件信息,每次數(shù)據(jù)變化的時候?qū)涞墓?jié)點重新計算,對所有的節(jié)點都同樣對待,而本文提出,在實際應用中,文件的使用頻率是有差別的,應該根據(jù)文件使用頻率來維護哈希樹的結構,提高頻繁使用的文件的優(yōu)先級,并提出了相應的解決方法,通過模型和實驗證明了本文提出的方法能夠有效減少在文件動態(tài)變化時的計算量,提高了效率。
[Abstract]:With the wide application of cloud computing, more and more users store their data and files on cloud servers. Cloud storage brings convenience to users, but also brings some problems. Users' data and files may be accidentally tampered with or lost on the server, and neither the user nor the cloud server will find them. Therefore, it is necessary to verify the user's data periodically to ensure the integrity and correctness of the user's data and files. This paper first summarizes the methods of cloud storage data verification, introduces the concept of data validation and the design principles of verification methods, and briefly introduces the existing methods. Then, aiming at the shortcomings of the existing methods, the author puts forward his own improvement ideas and methods. The existing verification methods can only validate the data of one user in each verification process, and this paper proposes a method to merge and verify the data of multiple users in the calculation process of one verification. Not only the times of verification are saved, but also the amount of data transmission between verifier and cloud storage is reduced. Then, aiming at the problem that the user's data will change dynamically, the existing verification methods use a hash tree called Merkle Hash tree to maintain file information, and recalculate the nodes of the tree every time the data changes. All nodes are treated the same, but this paper proposes that in practical applications, the frequency of file use is different, the structure of hash tree should be maintained according to the frequency of file use, and the priority of frequently used files should be raised. The corresponding solutions are put forward. The model and experiments show that the proposed method can effectively reduce the computational complexity and improve the efficiency when the file changes dynamically.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP333

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本文編號：1973792