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Towards a Data-Centric Approach to Attribution in the Cloud

  • Wenchao ZhouEmail author
Chapter

Abstract

With an increasing number of applications being mirgrated to cloud, it becomes evident that faults in these applications or the underlying cloud platform can be costly. In cases where a system fault occurs, administrators often find themselves needing to answer attribution questions, to perform a variety of managerial tasks including system debugging, accountability enforcement, and attack analysis. In this chapter, we propose Secure Time-Aware Provenance (STAP), a data-centric approach that provides the fundamental functionality required to answer such attribution questions—the capability to “explain” the existence (or change) of a certain distributed system state at a given time in a potentially adversarial environment.

The proposed STAP model allows consistent and complete explanations of system state (and changes) in dynamic environments, and can be efficiently maintained and queried even in potentially adversarial environments. STAPincorporates tamper-evident properties, and guarantees eventual detection of compromised nodes that lie or falsely implicate correct nodes.

Keywords

Distribute Hash Table Execution Trace Cloud Application Faulty Node Provenance Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The research work presented in this chapter are performed in collaboration with Boon Thau Loo, Andreas Haeberlan and Zachary Ives from University of Pennsylvania, and Micah Sherr from Georgetown University.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Georgetown UniversityWashington, DCUSA

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