Uniform Node Sampling Service Robust against Collusions of Malicious Nodes

Emmanuelle Anceaume (1, 2) , Yann Busnel (3) , Bruno Sericola (4)

1 CIDER
2 CIDRE - Confidentialité, Intégrité, Disponibilité et Répartition
3 GDD - Gestion de Données Distribuées [Nantes]
4 DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS

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.

Mots clés: randomized approximation algorithm.; Data stream; strong adversary; uniform sam- pling; Markov chains; randomized approximation algorithm

Domaines: Algorithme et structure de données [cs.DS]; Mathématique discrète [cs.DM]

Emmanuelle Anceaume, Yann Busnel, Bruno Sericola. Uniform Node Sampling Service Robust against Collusions of Malicious Nodes. 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2013), Jun 2013, Budapest, Hungary. pp.249. ⟨hal-00804430⟩