Title: A statistical mechanics approach to trust management in autonomic networks

Authors: Stefano Ermon

Addresses: Department of Computer Science, Cornell University, Upson Hall, 4142, Ithaca, NY, USA

Abstract: Trust management, broadly intended as the ability to maintain belief relationship among entities, is recognised as a fundamental security challenge for autonomous and self-organising networks. In this work, we focus on the evaluation process of trust evidence in distributed networks, where no pre-established infrastructure can be assumed. After casting the problem into the framework of estimation theory, a distributed maximum likelihood trust estimation algorithm is proposed. Strong parallels with spin glasses theory are shown, providing key insights about the algorithm performance and limitations, as well as useful formulas for parameters tuning. The problem is also formulated as an inference problem on a Markov random field, and an alternative fully distributed algorithm based on message passing techniques is then proposed. This work presents a mathematically rigorous analytical approach to the trust management problem, and proposes the use of statistical physics methods not only to understand the complex dynamics that arise from the interactions of peers in decentralised networks but also to design robust protocols whose performance can be rigorously evaluated.

Keywords: coordinated control; estimation; autonomic networks; message passing algorithms; belief propagation; communication networks; sensor networks; network analysis; network control; statistical mechanics; trust management; distributed networks.

DOI: 10.1504/IJSCC.2012.045930

International Journal of Systems, Control and Communications, 2012 Vol.4 No.1/2, pp.37 - 54

Published online: 23 Aug 2014 *

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