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Towards a Data-Centric Approach to Attribution in the Cloud

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Secure Cloud Computing

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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.

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Notes

  1. 1.

    After retrieving the recv entry based on VID and RTime, we use the STime (sender’s timestamp) attribute in recv to fetch the appropriate send entry on the sender’s side. This avoids explicit time synchronization.

  2. 2.

    MICROQUERY returns a single vertex; provenance queries must invoke it repeatedly to explore G ν . Hence the name.

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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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  1. Georgetown University, 37th & O St., Washington, DC, USA

    Wenchao Zhou

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Editors and Affiliations

  1. Center for Secure Information Systems, George Mason University, Fairfax, Virginia, USA

    Sushil Jajodia

  2. Center for Secure Information Systems, George Mason University, Fairfax, Virginia, USA

    Krishna Kant

  3. University of Milan, Crema, Italy

    Pierangela Samarati

  4. Computer Security Division, National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, USA

    Anoop Singhal

  5. The MITRE Corporation, McLean, Virginia, USA

    Vipin Swarup

  6. Computing and Information Science Division, Information Sciences Directorate, Triangle Park, North Carolina, USA

    Cliff Wang

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Zhou, W. (2014). Towards a Data-Centric Approach to Attribution in the Cloud. In: Jajodia, S., Kant, K., Samarati, P., Singhal, A., Swarup, V., Wang, C. (eds) Secure Cloud Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-9278-8_13

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