基于SaaS的隐私保护策略研究

发布时间：2018-05-08 20:20

  本文选题：软件作为服务 + 多租户　； 参考：《山东大学》2014年硕士论文

【摘要】：云计算是能够提供方便、按需网络来访问可配置计算资源的共享池的模型,它可以用最少的管理工作或者服务提供商的交互来快速配置和发布资源。软件即服务(Software as a Service,SaaS)作为云服务的一种模式,用户在本地不需要安装任何软件,可以按需向服务提供商进行定制,已经受到了广泛的认可。然而,对于SaaS而言,用户的信息是以远程的方式存储在SaaS服务提供商端,用户自身无法确保其隐私信息的安全性,这就突出了SaaS模式下隐私保护的重要性,给SaaS服务提供商提出的重大的挑战。与此同时,SaaS服务提供商需要提供部分资源来满足对租户信息的隐私保护。租户也会提出各自的隐私保护需求,其中包括隐私保护性能、隐私保护水平等,SaaS服务提供商需要尽可能的满足租户的需求,同时最大化自身的收益。 针对上述问题,本文首先从SaaS服务提供商的角度出发,结合SaaS多租户的特点,统筹考虑多租户提交的隐私保护需求和SaaS服务提供商现有资源数量,尽可能的提高SaaS服务提供商的收益。文章基于多目标优化理论来解决SaaS服务提供商与多租户隐私保护策略制定问题,构建了SaaS服务提供商隐私保护策略制定模型,围绕模型将隐私保护策略制定规约到多目标优化问题的求解,采用遗传算法解决规约成的多目标优化问题,得到最终的隐私保护策略。 对于多租户而言,他们希望尽可能的降低自己的花费,同时达到比较高的隐私保护性能；对于SaaS服务提供商而言,他希望在满足租户隐私保护需求的情况下,尽可能多的提高自己的收益。然后,高的隐私保护性能必然会增加租户的花费,同时也会增加SaaS服务提供商的资源开销。针对这一问题,为平衡SaaS服务提供商与多租户之间的利益关系,本文致力于建立与隐私保护性能,隐私保护等级关联的多租户和SaaS服务提供商的收益函数,根据纳什均衡博弈理论,将多租户和SaaS服务提供商确定隐私保护策略的过程规约为租户与SaaS进行博弈的过程。将租户的隐私保护约束信息作为均衡博弈的约束条件,建立纳什均衡博弈模型,通过对博弈模型的分析求解,最终得到适合多租户和SaaS服务提供商的隐私保护策略、租赁费用以及SaaS服务提供商总的收益。实验表明,本文提出的隐私保护博弈模型具有较好的可行性、有效性。
[Abstract]:Cloud computing is a model that provides convenient, on-demand networks to access shared pools of configurable computing resources. It can quickly configure and distribute resources with minimal administrative effort or service provider interaction. As a model of cloud services, users do not need to install any software locally and can be customized to service providers on demand, which has been widely accepted. However, for SaaS, the user's information is stored in a remote way in the SaaS service provider, the user itself can not ensure the security of their privacy information, which highlights the importance of privacy protection in SaaS mode. Major challenges for SaaS service providers. At the same time SaaS service providers need to provide some resources to meet the privacy protection of tenant information. Tenants will also put forward their privacy protection requirements, including privacy protection performance, privacy protection level and other SaaS service providers need to meet the needs of tenants as much as possible, while maximizing their own revenue. In view of the above problems, this paper firstly considers the privacy protection requirements submitted by multi-tenants and the number of existing resources of SaaS service providers from the point of view of SaaS service providers and the characteristics of SaaS multi-tenants. Increase SaaS service provider's revenue as much as possible. Based on the theory of multi-objective optimization, this paper solves the problem of SaaS service provider and multi-tenant privacy policy formulation, and constructs a model of SaaS service provider privacy policy formulation. According to the model, the privacy protection strategy is formulated to solve the multi-objective optimization problem, and the genetic algorithm is used to solve the multi-objective optimization problem, and the final privacy protection strategy is obtained. For multi-tenants, they want to keep their costs as low as possible while achieving higher privacy protection performance; for SaaS service providers, he wants to meet tenants' privacy needs. Increase your earnings as much as you can. Then, the high privacy protection performance will inevitably increase the tenant's expense, also will increase the SaaS service provider's resource overhead. In order to balance the benefit relationship between SaaS service provider and multi-tenant, this paper aims to establish the revenue function of multi-tenant and SaaS service provider associated with privacy protection performance and privacy protection level. According to Nash equilibrium game theory, the process of multi-tenant and SaaS service provider determining privacy protection policy is defined as the game process between tenant and SaaS. Taking the privacy protection constraint information of tenants as the constraint condition of equilibrium game, the Nash equilibrium game model is established. Through the analysis and solution of the game model, the privacy protection strategy suitable for multi-tenant and SaaS service provider is obtained. Rental fee and SaaS service provider's total revenue. Experiments show that the proposed game model is feasible and effective.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP309;TP393.09

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