RealNetworks | EPFL Graph Search

RealNetworks, Inc. is a provider of artificial intelligence and computer vision based products. RealNetworks was a pioneer in Internet streaming software and services. They are based in Seattle, Washington, United States. The company also provides subscription-based online entertainment services and mobile entertainment and messaging services. History RealNetworks (then known as Progressive Networks) was founded in 1994 by Rob Glaser, an ex-Microsoft executive, and a management team including Phil Barrett, Andy Sharpless, and Stephen Buerkle. The original goal of the company was to provide a distribution channel for politically progressive content. It evolved into a technology venture to leverage the Internet as an alternative distribution medium for audio broadcasts. Progressive Networks became RealNetworks in September 1997, in advance of the company's initial public offering (IPO) in October 1997 when shares of the company started trading on Nasdaq as "RNWK". RealNetwor

A Test-Bed for Misbehavior Detection in Mobile Ad-hoc Networks --- How Much Can Watchdogs Really Do?

Sonja Buchegger, Jean-Yves Le Boudec, Cédric Tissières

Several misbehavior detection and reputation systems have been proposed for mobile ad-hoc networks, relying on direct network observation mechanisms, so-called watchdogs. While these approaches have so far only been evaluated in simulations and restricted to selfish packet dropping, we are interested in the capabilities of a watchdog detection component in a real network. In this paper we present our test-bed implementation of misbehavior detection. Following an evaluation of both the feasibility and detectability of attacks on routing and forwarding in the Dynamic Source Routing (DSR) protocol, we present the design of our test-bed. In order to add detection capabilities, we extend the concept of passive acknowledgment by mechanisms for partial dropping, packet modification, and fabrication detection. We combine DSR with Netfilter and APE to enable detection. We implement both attackers and detection and show their feasibility and limitations.

2003

A Test-Bed for Misbehavior Detection in Mobile Ad-hoc Networks - How Much Can Watchdogs Really Do?

Sonja Buchegger, Jean-Yves Le Boudec, Cédric Tissières

2004

Privacy and Dynamics of Social Networks

Pedram Pedarsani

Over the past decade, investigations in different fields have focused on studying and understanding real networks, ranging from biological to social to technological. These networks, called complex networks, exhibit common topological features, such as a heavy-tailed degree distribution and the small world effect. In this thesis we address two interesting aspects of complex, and more specifically, social networks: (1) users’ privacy, and the vulnerability of a network to user identification, and (2) dynamics, or the evolution of the network over time. For this purpose, we base our contributions on a central tool in the study of graphs and complex networks: graph sampling. We conjecture that each observed network can be treated as a sample from an underlying network. Using this, a sampling process can be viewed as a way to observe dynamic networks, and to model the similarity of two correlated graphs by assuming that the graphs are samples from an underlying generator graph. We take the thesis in two directions. For the first, we focus on the privacy problem in social networks. There have been hot debates on the extent to which the release of anonymized information to the public can leak personally identifiable information (PII). Recent works have shown methods that are able to infer true user identities, under certain conditions and by relying on side information. Our approach to this problem relies on the graph structure, where we investigate the feasibility of de-anonymizing an unlabeled social network by using the structural similarity to an auxiliary network. We propose a model where the two partially overlapping networks of interest are considered samples of an underlying graph. Using such a model, first, we propose a theoretical framework for the de-anonymization problem, we obtain minimal conditions under which de-anonymization is feasible, and we establish a threshold on the similarity of the two networks above which anonymity could be lost. Then, we propose a novel algorithm based on a Bayesian framework, which is capable of matching two graphs of thousands of nodes - with no side information other than network structures. Our method has several potential applications, e.g., inferring user identities in an anonymized network by using a similar public network, cross-referencing dictionaries of different languages, correlating data from different domains, etc. We also introduce a novel privacy-preserving mechanism for social recommender systems, where users can receive accurate recommendations while hiding their profiles from an untrusted recommender server. For the second direction of this work, we focus on models for network growth, more specifically where the number of edges grows faster than the number of nodes, a property known as densification. The densification phenomenon has been recently observed in various real networks, and we argue that it can be explained simply through the way we observe (sample) networks. We introduce a process of sampling the edges of a fixed graph, which results in the super-linear growth of edges versus nodes, and show that densification arises if and only if the graph has a power-law degree distribution.

EPFL2013

Privacy and Dynamics of Social Networks

Pedram Pedarsani

EPFL2013