CamFlow: Managed Data-sharing for Cloud Services

A model of cloud services is emerging whereby a few trusted providers manage the underlying hardware and communications whereas many companies build on this infrastructure to offer higher level, cloud-hosted PaaS services and/or SaaS applications. From the start, strong isolation between cloud tenants was seen to be of paramount importance, provided first by virtual machines (VM) and later by containers, which share the operating system (OS) kernel. Increasingly it is the case that applications also require facilities to effect isolation and protection of data managed by those applications. They also require flexible data sharing with other applications, often across the traditional cloud-isolation boundaries; for example, when government provides many related services for its citizens on a common platform. Similar considerations apply to the end-users of applications. But in particular, the incorporation of cloud services within `Internet of Things' architectures is driving the requirements for both protection and cross-application data sharing. These concerns relate to the management of data. Traditional access control is application and principal/role specific, applied at policy enforcement points, after which there is no subsequent control over where data flows; a crucial issue once data has left its owner's control by cloud-hosted applications and within cloud-services. Information Flow Control (IFC), in addition, offers system-wide, end-to-end, flow control based on the properties of the data. We discuss the potential of cloud-deployed IFC for enforcing owners' dataflow policy with regard to protection and sharing, as well as safeguarding against malicious or buggy software. In addition, the audit log associated with IFC provides transparency, giving configurable system-wide visibility over data flows. [...]Comment: 14 pages, 8 figure

A Secure and Fair Resource Sharing Model for Community Clouds

Cloud computing has gained a lot of importance and has been one of the most discussed segment of today\u27s IT industry. As enterprises explore the idea of using clouds, concerns have emerged related to cloud security and standardization. This thesis explores whether the Community Cloud Deployment Model can provide solutions to some of the concerns associated with cloud computing. A secure framework based on trust negotiations for resource sharing within the community is developed as a means to provide standardization and security while building trust during resource sharing within the community. Additionally, a model for fair sharing of resources is developed which makes the resource availability and usage transparent to the community so that members can make informed decisions about their own resource requirements based on the resource usage and availability within the community. Furthermore, the fair-share model discusses methods that can be employed to address situations when the demand for a resource is higher than the resource availability in the resource pool. Various methods that include reduction in the requested amount of resource, early release of the resources and taxing members have been studied, Based on comparisons of these methods along with the advantages and disadvantages of each model outlined, a hybrid method that only taxes members for unused resources is developed. All these methods have been studied through simulations

Safer in the Clouds (Extended Abstract)

We outline the design of a framework for modelling cloud computing systems.The approach is based on a declarative programming model which takes the form of a lambda-calculus enriched with suitable mechanisms to express and enforce application-level security policies governing usages of resources available in the clouds. We will focus on the server side of cloud systems, by adopting a pro-active approach, where explicit security policies regulate server's behaviour.Comment: In Proceedings ICE 2010, arXiv:1010.530

Secure, reliable and dynamic access to distributed clinical data

An abundance of statistical and scientific data exists in the area of clinical and epidemiological studies. Much of this data is distributed across regional, national and international boundaries with different policies on access and usage, and a multitude of different schemata for the data often complicated by the variety of supporting clinical coding schemes. This prevents the wide scale collation and analysis of such data as is often needed to infer clinical outcomes and to determine the often moderate effect of drugs. Through grid technologies it is possible to overcome the barriers introduced by distribution of heterogeneous data and services. However reliability, dynamicity and fine-grained security are essential in this domain, and are not typically offered by current grids. The MRC funded VOTES project (Virtual Organisations for Trials and Epidemiological Studies) has implemented a prototype infrastructure specifically designed to meet these challenges. This paper describes this on-going implementation effort and the lessons learned in building grid frameworks for and within a clinical environment