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Bluebear : Exploration des risques d’atteinte à la vie privée sur Internet

Arnaud Legout

10 mars 2011 de 11h à 12h : salle 25-26/101

Les risques d’atteinte à la vie privée sur Internet sont largement sous estimés. Il est connu que les internautes laissent énormément d’informations personnelles sur, par exemple, les réseaux sociaux. Cependant, peu de gens savent que toute activité sur Internet laisse des traces. Dans cet exposé, nous allons montrer qu’il est possible de collecter ces traces à grande échelle et sans infrastructure dédiée. Plus précisément, nous allons montrer qu’il est possible de surveiller l’activité sur BitTorrent de centaines de millions d’internautes. Nous allons également montrer qu’en exploitant des mesures faites sur BitTorrent et sur un réseaux de voix sur IP, il est non seulement possible de lier à une identité réelle une liste de téléchargement de manière automatique, mais qu’il est également possible de suivre les déplacements d’un grand nombre de personnes sans que ces personnes n’aient aucun moyen de détecter ni de bloquer ces mesures.

Learning Propagation Schemes in Multi-Graphs

Ludovic Denoyer

10 février 2011 de 11h à 12h : salle 25-26/101

We consider the problem of labeling nodes in a multi-graph where the nodes may be connected with different types of relations. This type of problem occurs in many situations like for example the analysis of social networks or bibliographic data. Relations may be provided (e.g. friends) or computed (e.g. similarity). We propose a new learning algorithm for exploiting the rich multi-relational information in the labeling task. This method is able to optimally learn combining the influence of different relation types. It is one of the very first algorithms able to handle multi-graph information for this classi fication task. We describe experiments on four datasets which show the model ability to deal with complex relationships and to take bene fit of multi-relational information for complex labeling problems.

Interactive multiscale visualization of huge graphs: application to a network of weblogs

Massoud Seifi, Jean-loup Guillaume, Matthieu Latapy and Bénédicte Le Grand

Proceedings of the 8th Workshop on Visualization and Knowledge Extraction (EGC 2010)

De nombreux réseaux du monde réel peuvent être modélisés par des grands graphes. Réduire la complexité d’un graphe de manière à ce qu’il puisse être facilement interprété par l’oeil humain est une aide précieuse pour comprendre et analyser ce type de données. Nous proposons une méthodologie de visualisation interactive multi-échelle de grands graphes basée sur une classification des sommets qui nous permet de représenter ces graphes de manière lisible et interprétable. Nous appliquons notre méthodologie à un réseau de blogs francophones.

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Impact of Random Failures and Attacks on Poisson and Power-Law Random Networks

Clémence Magnien, Matthieu Latapy and Jean-Loup Guillaume

ACM Computing Surveys, 43 (3), 2011. Extended abstract published in LNCS, proceedings of the 8-th International Conference On Principles Of Distributed Systems OPODIS’04, 2004, Grenoble, France (title: Comparison of Failures and Attacks on Random and Scale-free Networks)

It appeared recently that the underlying degree distribution of a network may play a crucial role concerning its robustness. Empiric and analytic results have been obtained, based on asymptotic and mean-field approximations. Previous work insisted on the fact that power-law degree distributions induce a high resilience to random failure but a high sensitivity to some attack strategies, while Poisson degree distributions are quite sensitive in both cases. Then, much work has been done to extend these results. We focus here on these basic results, with the aim of deepening significantly our understanding of their origin and their limitations. We review in detail previous contributions and we give full proofs in a unified framework, in which the approximations on which these results rely are well identified. We then add to these known results a set of new ones aimed at enlightening some important aspects. We also provide extensive and rigorous experiments which make it possible to evaluate the relevance of the analytic results. We reach the conclusion that, even if it is clear that the basic results of the field are true and important, they are in practice much less striking than generally thought. The differences between random failures and attacks are not so huge and can be explained with simple facts. Likewise, the differences in the behaviors induced by power-law and Poisson distributions are not as striking as often claimed.

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Mobile IPv6 Deployments: Graph-based Analysis and practical Guidelines

Guillaume Valadon, Clémence Magnien and Ryuji Wakikawa

Computer Communications, 32, 2009

The Mobile IPv6 protocol is a major solution to supply mobility services on the Internet. Many networking vendors have already implemented it in their operating systems and equipments. Moreover, it was recently selected to provide permanent IP addresses to end users of WiMAX and 3GPP2. Mobile IPv6 relies on a specific router called the home agent that hides location changes of the mobile nodes from the rest of the Internet. To do so, the mobile nodes’ traffic must flow through the home agent. This mandatory deviation produces longer paths and higher communication delays. In order to solve these problems, we describe a new approach to address deployments of Mobile IPv6 based on graph theory and could be applied to any operator’s network. In particular, we use notions of centrality in graphs to quantify increases of communication distances induced by dogleg routing and identify relevant home agents locations. We evaluate this approach using real-world network topologies and show that the obtained Mobile IPv6 performance could be close to direct paths ones. The proposed algorithm is generic and can be used to achieve efficient deployments of Mobile IPv6 as well as Home Agent Migration: a new Mobile IPv6 architecture using several distributed home agents.

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