Uniform node sampling service robust against collusions of malicious nodes

We consider the problem of achieving uniform node sampling in large scale systems in presence of a strong adversary. We first propose an omniscient strategy that processes on the fly an unbounded and arbitrarily biased input stream made of node identifiers exchanged within the system, and outputs a...

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Veröffentlicht in:2013 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) S. 1 - 12
Hauptverfasser: Anceaume, Emmanuelle, Busnel, Yann, Sericola, Bruno
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2013
Schlagworte:
Data stream
Iris
Markov chains
randomized approximation algorithm
strong adversary
uniform sampling
ISBN:9781467364713, 1467364711
ISSN:1530-0889
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Zusammenfassung:We consider the problem of achieving uniform node sampling in large scale systems in presence of a strong adversary. We first propose an omniscient strategy that processes on the fly an unbounded and arbitrarily biased input stream made of node identifiers exchanged within the system, and outputs a stream that preserves Uniformity and Freshness properties. We show through Markov chains analysis that both properties hold despite any arbitrary bias introduced by the adversary. We then propose a knowledge-free strategy and show through extensive simulations that this strategy accurately approximates the omniscient one. We also evaluate its resilience against a strong adversary by studying two representative attacks (flooding and targeted attacks). We quantify the minimum number of identifiers that the adversary must insert in the input stream to prevent uniformity. To our knowledge, such an analysis has never been proposed before.
ISBN:9781467364713
1467364711
ISSN:1530-0889
DOI:10.1109/DSN.2013.6575363