Access control is an important component for web services such as a cloud. Current clouds tend to design the access control mechanism together with the policy language on their own. It leads to two issues: (i) a cloud user has to learn different policy languages to use multiple clouds, and (ii) a cloud service provider has to customize an authorization mechanism based on its business requirement, which brings high development cost. In this work, a new access control policy language called PERM modeling language (PML) is proposed to express various access control models such as access control list (ACL), role-based access control (RBAC) and attribute-based access control (ABAC), etc. PML's enforcement mechanism is designed in an interpreter-on-interpreter manner, which not only secures the authorization code with sandboxing, but also extends PML to all programming languages that support Lua. PML is already adopted by real-world projects such as Intel's RMD, VMware's Dispatch, Orange's Gobis and so on, which proves PML's usability. The performance evaluation on OpenStack, CloudStack and Amazon Web Services (AWS) shows PML's enforcement overhead per request is under 5.9us.
Access control as a service (ACaaS), proposed in @cite_2 by , provided a new cloud service to provide comprehensive and fine-grained access control. It is claimed to support multiple access control models, whereas there is no evidence that this approach applies to the models except RBAC. And this work is highly based on IAM provided by AWS, which makes it difficult to apply for other clouds.
@cite_2
2014540924 1992204376 2164365844 1544010666
Organizations and enterprises have been outsourcing their computation, storage, and workflows to Infrastructure-as-a-Service (IaaS) based cloud platforms. The heterogeneity and high diversity of IaaS cloud environment demand a comprehensive and fine-grained access control mechanism, in order to meet dynamic, extensible, and highly configurable security requirements of these cloud consumers. However, existing security mechanisms provided by IaaS cloud providers do not satisfy these requirements. To address such an emergent demand, we propose a new cloud service called access control as a service (ACaaS), a service-oriented architecture in cloud to support multiple access control models, with the spirit of plug gable access control modules in modern operating systems. As a proof-of-concept reference prototype, we design and implement ACaaS_RBAC to provide role-based access control (RBAC) for Amazon Web Services (AWS), where cloud customers can easily integrate the service into enterprise applications in order to extend RBAC policy enforcement in AWS. Cloud Infrastructure as a Service (IaaS), where traditional IT infrastructure resources such as compute, storage and networking are owned by a cloud service provider (CSP) and offered as on-demand virtual resources to customers (tenants), is the fastest maturing service model in cloud computing. The transformation of physical resources into virtual offers great flexibility to CSP customers including network based remote collaborative administration. This flexibility can be fully availed only if complemented by commensurately flexible access control to the customers remote IT resources by the customer's IT users. Since customer policies in this regard can vary greatly, the CSP needs a flexible model to accommodate diverse policy requirements. In this paper, we investigate attribute-based access control (ABAC) in cloud IaaS. In ABAC, access requests are evaluated based on the attributes of cloud tenant users and those of objects such as virtual machines, storage volumes, networks, etc. We investigate the access control models supported by commercial IaaS providers such as Amazon AWS and opensource OpenStack, as well as other models in the literature, which mostly use role-based access control (RBAC). We demonstrate their limitations and motivate the need for ABAC support to realize the true potential of IaaS. Building on prior published ABAC models we define a formal ABAC model suitable for IaaS. As proof-of-concept we implement this model in OpenStack, a widely-used open source cloud IaaS software platform. We discuss enforcement alternatives in this context and partially evaluate their performance. Emerging storage cloud systems provide continuously available and highly scalable storage services to millions of geographically distributed clients. A secure access control mechanism is a crucial prerequisite for allowing clients to entrust their data to such cloud services. The seamlessly unlimited scale of the cloud and the new usage scenarios that accompany it pose new challenges in the design of such access control systems. In this paper we present a capability-based access control model and architecture appropriate for cloud storage systems that is secure, flexible, and scalable. We introduce new functionalities such as a flexible and dynamic description of resources; an advanced delegation mechanism and support for auditability, accountability and access confinement. The paper details the secure access model, shows how it fits in a scalable storage cloud architecture, and analyzes its security and performance. This paper introduces a new transactional “database-as-a-service” (DBaaS) called Relational Cloud. A DBaaS promises to move much of the operational burden of provisioning, configuration, scaling, performance tuning, backup, privacy, and access control from the database users to the service operator, offering lower overall costs to users. Early DBaaS efforts include Amazon RDS and Microsoft SQL Azure, which are promising in terms of establishing the market need for such a service, but which do not address three important challenges: efficient multi-tenancy, elastic scalability, and database privacy. We argue that these three challenges must be overcome before outsourcing database software and management becomes attractive to many users, and cost-effective for service providers. The key technical features of Relational Cloud include: (1) a workload-aware approach to multi-tenancy that identifies the workloads that can be co-located on a database server, achieving higher consolidation and better performance than existing approaches; (2) the use of a graph-based data partitioning algorithm to achieve near-linear elastic scale-out even for complex transactional workloads; and (3) an adjustable security scheme that enables SQL queries to run over encrypted data, including ordering operations, aggregates, and joins. An underlying theme in the design of the components of Relational Cloud is the notion of workload awareness: by monitoring query patterns and data accesses, the system obtains information useful for various optimization and security functions, reducing the configuration effort for users and operators.