How to Track Your Data: Rule-Based Data Provenance Tracing Algorithms

Abstract

As cloud computing and virtualization technologies become mainstream, the need to be able to track data has grown in importance. Having the ability to track data from its creation to its current state or its end state will enable the full transparency and accountability in cloud computing environments. In this paper, we showcase a novel technique for tracking end-to-end data provenance, a meta-data describing the derivation history of data. This breakthrough is crucial as it enhances trust and security for complex computer systems and communication networks. By analyzing and utilizing provenance, it is possible to detect various data leakage threats and alert data administrators and owners; thereby addressing the increasing needs of trust and security for customers' data. We also present our rule-based data provenance tracing algorithms, which trace data provenance to detect actual operations that have been performed on files, especially those under the threat of leaking customers' data. We implemented the cloud data provenance algorithms into an existing software with a rule correlation engine, show the performance of the algorithms in detecting various data leakage threats, and discuss technically its capabilities and limitations.

DOI: 10.1109/TrustCom.2012.175

Extracted Key Phrases

19 Figures and Tables

Cite this paper

@article{Zhang2012HowTT, title={How to Track Your Data: Rule-Based Data Provenance Tracing Algorithms}, author={Olive Qing Zhang and Ryan K. L. Ko and Markus Kirchberg and Chun Hui Suen and Peter Jagadpramana and Bu-Sung Lee}, journal={2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications}, year={2012}, pages={1429-1437} }