本文選題：云安全 + 安全樸素貝葉斯分類　； 參考：《南京航空航天大學(xué)》2017年碩士論文

【摘要】：隨著云計算的不斷普及,多樣的云服務(wù)為用戶的工作生活帶來了各種便利。典型的云服務(wù)是數(shù)據(jù)外包,數(shù)據(jù)擁有者將其擁有的大量數(shù)據(jù)外包到云服務(wù)器中存儲,減少自身所需的存儲管理花費。通常為了保護自身數(shù)據(jù)的隱私,擁有者將私有數(shù)據(jù)加密后再上傳到云服務(wù)器中。同時,擁有者也希望利用云強大的計算能力對存儲在云中的加密數(shù)據(jù)進行分析,獲取原始數(shù)據(jù)間的關(guān)聯(lián)信息。然而加密會使得這些數(shù)據(jù)分析變得十分困難。在不泄露數(shù)據(jù)隱私的前提下對加密數(shù)據(jù)進行安全計算成為當前的研究熱點。本文的主要工作如下:1、研究了加密數(shù)據(jù)上的安全樸素貝葉斯分類問題。通過引入兩個不共謀的云服務(wù)器模型并運用加法同態(tài)加密和安全多方計算,本文提出了一個安全樸素貝葉斯分類協(xié)議。與現(xiàn)有的方案相比,該協(xié)議將樸素貝葉斯的所有計算任務(wù)都外包給了云服務(wù)器,并且云服務(wù)器無法從整個計算過程中獲得數(shù)據(jù)擁有者訓(xùn)練數(shù)據(jù)集、貝葉斯分類器參數(shù)以及用戶測試樣本的隱私信息。分析了該協(xié)議的安全性,并通過理論分析以及模擬實驗分析了計算以及通信復(fù)雜度。2、對加密數(shù)據(jù)安全比較這一加密數(shù)據(jù)安全計算領(lǐng)域的基本問題進行了研究。通過利用加法同態(tài)加密以及姚式亂碼電路,提出了一種高效的加密數(shù)據(jù)安全比較協(xié)議。并且基于提出的安全比較協(xié)議,設(shè)計了一個高效的安全范圍查詢協(xié)議。本文提出的安全比較協(xié)議和安全范圍查詢協(xié)議與已有的方案相比具有更高的效率,并且在半誠實模型下是安全的,能夠保證輸入輸出數(shù)據(jù)的隱私信息。分析了這兩個協(xié)議的安全性,通過理論分析以及模擬實驗進行了性能分析與評估。
[Abstract]:With the increasing popularity of cloud computing, a variety of cloud services for users to bring a variety of work and life convenience. The typical cloud service is data outsourcing. The data owner outsource a large amount of data to the cloud server to reduce the cost of storage management. In order to protect the privacy of their own data, the owner encrypts the private data and uploads it to the cloud server. At the same time, the owner also wants to use the powerful computing power of the cloud to analyze the encrypted data stored in the cloud to obtain the correlation information between the original data. Encryption, however, makes this data analysis very difficult. Security calculation of encrypted data without revealing data privacy has become a hot research topic. The main work of this paper is as follows: 1. We study the classification of secure naive Bayes on encrypted data. By introducing two non-collusive cloud server models and using additive homomorphic encryption and secure multi-party computation, a secure naive Bayes classification protocol is proposed in this paper. Compared with the existing schemes, the protocol outsources all computing tasks of naive Bayes to cloud servers, and cloud servers cannot obtain data owner training data sets from the entire computing process. Bayesian classifier parameters and privacy information of user test samples. The security of the protocol is analyzed and the computational and communication complexity of the protocol is analyzed by theoretical analysis and simulation experiments. The basic problem of encryption data security comparison in the field of encrypted data security computing is studied. By using additive homomorphism encryption and Yao chaotic code circuit, an efficient encryption data security comparison protocol is proposed. Based on the proposed security comparison protocol, an efficient security range query protocol is designed. The security comparison protocol and the security range query protocol proposed in this paper are more efficient than the existing schemes, and are secure in the semi-honest model, which can guarantee the privacy information of the input and output data. The security of the two protocols is analyzed and the performance is analyzed and evaluated by theoretical analysis and simulation experiments.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP309;TP393.09

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本文編號：2049159