本文選題：Saas　切入點(diǎn)：隱私保護(hù)　出處：《山東大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：軟件即服務(wù),Software-as-a-Service,伴隨著云計算的發(fā)展而逐漸普及起來。企業(yè)等租戶利用SaaS提供的服務(wù)處理業(yè)務(wù),省去了軟件安裝與維護(hù)的人力、財力。同時,多租戶根據(jù)資源需求和租賃時間繳納費(fèi)用,減少了因為處理少量數(shù)據(jù)而專門購買大型軟件所造成的資源和資金的浪費(fèi)。然而,SaaS應(yīng)用的軟件安裝在本地服務(wù)器上,租戶若要使用SaaS服務(wù),必須把自己的數(shù)據(jù)存放在SaaS服務(wù)提供商的服務(wù)器上。如果對這些數(shù)據(jù)不加以處理,則公司機(jī)密信息與個人的隱私數(shù)據(jù)就面臨著泄漏的可能,數(shù)據(jù)安全問題受到威脅。因此如何有效的解決數(shù)據(jù)隱私問題成為當(dāng)下各研究機(jī)構(gòu)、專家學(xué)者的關(guān)注�，F(xiàn)有的隱私保護(hù)方法主要分為兩種,數(shù)據(jù)加密與數(shù)據(jù)混淆。數(shù)據(jù)加密是基于數(shù)學(xué)上難解的問題或不可逆的過程為算法對數(shù)據(jù)進(jìn)行變形,使得數(shù)據(jù)即使泄漏攻擊者也難以理解數(shù)據(jù)本身的意義。數(shù)據(jù)混淆是通過泛化或者匿名的方式隱藏原有數(shù)據(jù)信息。其中泛化是將離散的數(shù)據(jù)值擴(kuò)展為一段連續(xù)的數(shù)據(jù)區(qū)間,該數(shù)據(jù)區(qū)間包含原數(shù)據(jù)值,匿名是通過劃分或者摻沙的方式保證每個分組內(nèi)的數(shù)據(jù)等價,以此來隱藏數(shù)據(jù)分布的信息。然而為了提高隱私保護(hù)程度,加密算法一般設(shè)計的較為復(fù)雜,進(jìn)而加密解密需要的計算時間較長,這對于即時的SaaS應(yīng)用來說不可接受。數(shù)據(jù)混淆較數(shù)據(jù)加密的計算速度要快,但是存在原有數(shù)據(jù)無法重構(gòu)的缺陷,并且會產(chǎn)生臟數(shù)據(jù)。針對上述問題和挑戰(zhàn),本文提出數(shù)據(jù)劃分的概念。數(shù)據(jù)劃分是指將租戶身份信息(如姓名、身份證號、社保號)與隱私數(shù)據(jù)(如疾病、薪資)垂直分割到不同數(shù)據(jù)分塊中,混淆之間的對應(yīng)關(guān)系。該方法相對于數(shù)據(jù)加密效率高,同時可以根據(jù)數(shù)據(jù)分塊間的對應(yīng)關(guān)系重構(gòu)租戶的原始數(shù)據(jù)邏輯結(jié)構(gòu),避免了租戶數(shù)據(jù)失真的問題。數(shù)據(jù)劃分粒度越細(xì),數(shù)據(jù)安全程度越高,然而組合數(shù)據(jù)花費(fèi)的時間也越多。如何對數(shù)據(jù)進(jìn)行合理的劃分使得用戶的隱私得到保障的同時盡可能地提高應(yīng)用的響應(yīng)速度成為一種挑戰(zhàn)。本文通過統(tǒng)計用戶對數(shù)據(jù)的訪問模式生成屬性關(guān)聯(lián)度矩陣,使用鍵能算法對屬性關(guān)聯(lián)度矩陣進(jìn)行聚類,以用戶個性化提出的隱私約束作為限制條件對聚類后的矩陣進(jìn)行分割,從而生成最優(yōu)隱私劃分策略,該策略下得到的數(shù)據(jù)分塊上應(yīng)用操作所需的連接次數(shù)最少,性能最好。相同的服務(wù)資源下,隱私保護(hù)程度的提高以犧牲一定的計算速度為前提,應(yīng)用服務(wù)的計算速度與內(nèi)存、CPU等計算資源量成正相關(guān)關(guān)系。在計算資源一定的情況下,如何分配給不同用戶使得各用戶在滿足自身的隱私保護(hù)需求的前提下,最大化資源利用率。本文通過獨(dú)立定價算法跟集中定價算法兩階段競價機(jī)制,全局調(diào)控用戶的資源申請量。獨(dú)立定價參考同需求(或需求相近)的歷史定價數(shù)據(jù),根據(jù)資源占比情況快速給出價格；集中定價策略根據(jù)多租戶提出的資源申請量與隱私保護(hù)服務(wù)需求建立效用函數(shù),并使用多目標(biāo)粒子群算法根據(jù)最優(yōu)解的帕累托支配關(guān)系求解出最佳資源分配與定價。
[Abstract]:Software as a service, Software-as-a-Service, with the development of cloud computing and the increasing popularity of services for business enterprises. The tenant provided by SaaS, eliminating the need for software installation and maintenance of the human resources. At the same time, according to the multi tenant resource requirements and pay a fee for the lease time, reduced because of a small amount of data processing and specializes in buying large software caused by the waste of money and resources. However, the application of SaaS software installed on the local server, the tenants to use the SaaS service, we must put our own data stored in the server SaaS service provider. If the data is not addressed, the privacy data is confidential information and personal face leakage may, the problem of data security is threatened. So how to effectively solve the problem of data privacy has become the current research institutions, experts and scholars pay attention to existing privacy. The main protection methods are divided into two types, mixed data encryption and data. Data encryption is a mathematical puzzle or irreversible process is based on the algorithm of data distortion, even if the attacker makes data leakage is also difficult to understand the meaning of the data itself. Data obfuscation is hidden information of original data through universal or anonymous way. The generalization is discrete data values for a continuous expansion of data interval, the interval data contains the original data values, anonymous data guarantee equivalence in each group by dividing or sand mixing way, in order to hide the information of the data distribution. However, in order to improve the degree of privacy protection, the general design of the complicated encryption algorithm then, the encryption and decryption of longer computation time required for the SaaS application, the real-time data obfuscation is not acceptable. The calculation speed faster than the data encryption, but is There are shortcomings of the original data can not be reconstructed, and will produce the dirty data. Aiming at the above problems and challenges, this paper puts forward the concept of data partitioning. Data division refers to the tenant identity information (such as name, ID number, social security number) and private data (such as disease, salary) vertically divided into different data blocks the corresponding relationship between the confusion, the data encryption method. Compared with high efficiency, and can be based on the original data points corresponding to reconstruct the relationship between the logical structure of the data block between the tenants and tenants to avoid the distortion of data. Data granularity is finer, the higher the degree of data security, however, the combined data time spent on how to more. The data were reasonably divided so that the user's privacy is guaranteed as far as possible to improve the response speed of the application has become a challenge. Through the statistics of user access patterns of data is generated Of the correlation matrix, can use the key algorithm to cluster the attribute correlation matrix, put forward to user privacy constraints as matrix after clustering limit conditions to generate the optimal segmentation, privacy partition strategy, the strategy of data block on the application of operating connection required minimum number of the best performance. The same service resources, improve the degree of privacy protection at the expense of calculation speed as the premise, computing speed and memory applications, CPU is a positive correlation between the amount of computing resources in the computational resources. Under certain circumstances, how to make different users assigned to each user in the premise of meeting the demand of their own under the protection of privacy and maximize the utilization of resources. Through the independent pricing algorithm with centralized pricing algorithm two stage bidding mechanism, control user resource application independent pricing reference with demand (. Or similar) historical pricing data, according to the proportion of resources given the rapid price; pricing strategy is established according to the utility function of multi tenant application resources and the protection of privacy and service needs, using multi-objective particle swarm optimization algorithm based on the optimal solution of the Pareto relation for the optimal resource allocation and pricing.

【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP309

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相關(guān)期刊論文 前1條

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本文編號：1651724