Yuanjun Yao

Qiang Cao, Yuanjun Yao, Jeff Chase

Federated clouds raise a variety of challenges for managing identity, resource access, naming, connectivity, and object access control. This paper shows how to address these challenges in a comprehensive and uniform way using a data-centric approach. The foundation of our approach is a trust logic in which participants issue authenticated statements about principals, objects, attributes, and relationships in a logic language, with reasoning based on declarative policy rules. We show how to use the logic to implement a trust infrastructure for cloud federation that extends the model of NSF GENI, a federated IaaS testbed. It captures shared identity management, GENI authority services, cross-site interconnection using L2 circuits, and a naming and access control system similar to AWS Identity and Access Management (IAM), but extended to a federated system without central control.

Qiang Cao, Vamsi Thummala, Jeffrey S. Chase et al.

SAFE is a data-centric platform for building multi-domain networked systems, i.e., systems whose participants are controlled by different principals. Participants make trust decisions by issuing local queries over logic content exchanged in certificates. The contribution of SAFE is to address a key barrier to practical use of logical trust: the problem of identifying, gathering, and assembling the certificates that are relevant to each trust decision. SAFE uses a simple linking abstraction to organize and share certificates according to scripted primitives that implement the application's trust kernel and isolate it from logic concerns. We show that trust scripting with logical data exchange yields compact trust cores for example applications: federated naming, nested groups and roles, secure IP prefix delegation and routing, attestation-based access control, and a federated infrastructure-as-a-service system. Linking allows granular control over dynamic logic content based on dependency relationships, enabling a logic server to make secure inferences at high throughput.