Research On Privacy Protection Strategy For SAAS

Cloud computing is a model which has the ability to provide a convenient, on-demand network to access a resource-shared pool for allocating computing resources. Through cloud computing, we can use minimal management work or service provider interaction to quickly configure and publish resources. For SaaS (Software as a service), as a model of cloud service, users do not need to install any software locally, they can customize on-demand from service provider, which has been widely recognized. However, for SaaS, users’information is stored in the remote SaaS service provider side. Users can not ensure the security of their own privacy information, which highlights the importance of privacy protection under the SaaS model and raises a major challenge to SaaS service providers. At the same time, SaaS service providers need to provide some resources to meet the privacy protection of tenants’information. Tenants will put forward their own privacy protection requirements which including privacy protection performance, privacy protection level, and so on. SaaS service providers need to meet the tenant’s needs as much as possible, while maximizing their benefits.In response to these problems, this paper firstly considers from the perspective of SaaS service providers, considers both the characteristics of multi-tenants’ privacy protection requirements and the amount of resources available of service providers as a whole, in order to improve SaaS service providers’income as much as possible. Based on multi-objective optimization theory, we try to make SaaS service providers’and multi-tenants’privacy protection strategy, develop SaaS service providers’the privacy protection strategy model. According the model, we transfer the privacy protection strategy formulation to the solution of a multi-objective optimization problem, and then take the genetic algorithm to solve multi-objective optimization problem, at last get the privacy protection strategy. Related experiments verify the efficiency of the algorithm and describe the process of developing the privacy protection strategy.Multi-tenants and SaaS providers reach the privacy protection strategy after negotiation, due to the limit of SaaS service provider virtual machine total resources, tenants need to compete resources in order to meet their own SaaS application performance and privacy protection needs, and they want to spend less to achieve better privacy protection and application performance. SaaS service providers need to rational allocate resources to ensure their revenue maximization under the premise of the needs of tenants. Therefore, multi-tenants and SaaS service providers compete with each other on performance requirements and resource. We take into account the privacy protection performance and privacy needs of tenants, at the same time, consider the interests of both SaaS providers and tenants, introduce the Nash equilibrium game theory to balance the interests of both sides. The purpose is to achieve the equilibrium that any party cannot only change its own privacy policy to increase revenue or reduce cost. Finally, the ultimate privacy protection policy is determined by the tenants’privacy indicators in the equilibrium state. Related experiments verify the efficiency of the algorithm and describe the process of developing the privacy protection strategy.