Uniform and Ergodic Sampling in Unstructured Peer-to-Peer Systems with Malicious Nodes

Emmanuelle Anceaume 1 Yann Busnel 2 Sebastien Gambs 1
1 ADEPT - Algorithms for Dynamic Dependable Systems
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, INRIA Rennes
Abstract : We consider the problem of uniform sampling in large scale open systems. Uniform sampling is a fundamental schema that guarantees that any individual in a population has the same probability to be selected as sample. An important issue that seriously hampers the feasibility of uniform sampling in open and large scale systems is the inevitable presence of malicious nodes. In this paper we show that restricting the number of requests that malicious nodes can issue and allowing for a full knowledge of the composition of the system is a necessary and sufficient condition to guarantee uniform and ergodic sampling. In a nutshell, a uniform and ergodic sampling guarantees that any node in the system is equally likely to appear as a sample at any non malicious node in the system and that infinitely often any nodes have a non null probability to appear as a sample at any honest nodes.
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Emmanuelle Anceaume, Yann Busnel, Sebastien Gambs. Uniform and Ergodic Sampling in Unstructured Peer-to-Peer Systems with Malicious Nodes. 14th International Conference On Principles Of Distributed Systems (OPODIS 2010), Dec 2010, Tozeur, Tunisia. pp.64--78, ⟨10.1007/978-3-642-17653-1_5⟩. ⟨hal-00554219⟩

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