Abstract
Reputation systems play a vital role in constructing mutual trust relationships between different entities in autonomic computing networks by enforcing them to act as prescribed protocols or specifications. They can be, however, subverted and abused if the association rules between an entity’s identity and its reputation is exploited. While various anonymizating techniques can be used to prevent that, the extent of anonymity is extremely hard to be determined at an appropriate level, potentially allowing sophisticated attackers to correlate a party with its reputation. To manifest and further gain insights into such vulnerabilities, we systematically decompose the reputation system into four components from a functional perspective and use a set of performance metrics to examine them. Specifically, a new attack taxonomy is given, and a simple scheme termed STARS, which is transparent to particular reputation systems, is proposed for achieving both anonymity and traceability. We finally discuss implementation issues and validate performance through case studies, comparative analysis, and simulations.
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Zhang, Z., Liu, J., Kadobayashi, Y. (2011). STARS: A Simple and Efficient Scheme for Providing Transparent Traceability and Anonymity to Reputation Systems. In: Garcia-Alfaro, J., Navarro-Arribas, G., Cavalli, A., Leneutre, J. (eds) Data Privacy Management and Autonomous Spontaneous Security. DPM SETOP 2010 2010. Lecture Notes in Computer Science, vol 6514. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19348-4_13
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DOI: https://doi.org/10.1007/978-3-642-19348-4_13
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