Privacy Preserving Detection of Path Bias Attacks in Tor

Anonymous communication networks like Tor are vulnerable to attackers that control entry and exit nodes. Such attackers can compromise the essential anonymity and privacy properties of the network. In this paper, we consider the path bias attack– where the attacker induces a client to use compromised nodes and thus links the client to their destination. We describe an efficient scheme that detects such attacks in Tor by collecting routing telemetry data from nodes in the network. The data collection is differentially private and thus does not reveal behaviour of individual users even to nodes within the network. We show provable bounds for the sample complexity of the scheme and describe methods to make it resilient to introduction of false data by the attacker to subvert the detection process. Simulations based on real configurations of the Tor network show that the method works accurately in practice

Applications of Derandomization Theory in Coding

Randomized techniques play a fundamental role in theoretical computer science and discrete mathematics, in particular for the design of efficient algorithms and construction of combinatorial objects. The basic goal in derandomization theory is to eliminate or reduce the need for randomness in such randomized constructions. In this thesis, we explore some applications of the fundamental notions in derandomization theory to problems outside the core of theoretical computer science, and in particular, certain problems related to coding theory. First, we consider the wiretap channel problem which involves a communication system in which an intruder can eavesdrop a limited portion of the transmissions, and construct efficient and information-theoretically optimal communication protocols for this model. Then we consider the combinatorial group testing problem. In this classical problem, one aims to determine a set of defective items within a large population by asking a number of queries, where each query reveals whether a defective item is present within a specified group of items. We use randomness condensers to explicitly construct optimal, or nearly optimal, group testing schemes for a setting where the query outcomes can be highly unreliable, as well as the threshold model where a query returns positive if the number of defectives pass a certain threshold. Finally, we design ensembles of error-correcting codes that achieve the information-theoretic capacity of a large class of communication channels, and then use the obtained ensembles for construction of explicit capacity achieving codes. [This is a shortened version of the actual abstract in the thesis.]Comment: EPFL Phd Thesi

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

Mining, Modeling and Predicting Mobility

Mobility is a central aspect of our life, and our movements reveal much more about us than simply our whereabouts. In this thesis, we are interested in mobility and study it from three different perspectives: the modeling perspective, the information-theoretic perspective, and the data mining perspective. For the modeling perspective, we represent mobility as a probabilistic process described by both observable and latent variables, and we introduce formally the notion of individual and collective dimensions in mobility models. Ideally, we should take advantage of both dimensions to learn accurate mobility models, but the nature of data might limit us. We take a data-driven approach to study three scenarios, which differ on the nature of mobility data, and present, for each scenario, a mobility model that is tailored for it. The first scenario is individual-specific as we have mobility data about individuals but are unable to cross reference data from them. In the second scenario, we introduce the collective model that we use to overcome the sparsity of individual traces, and for which we assume that individuals in the same group exhibit similar mobility patterns. Finally, we present the ideal scenario, for which we can take advantage of both the individual and collective dimensions, and analyze collective mobility patterns in order to create individual models. In the second part of the thesis, we take an information-theoretic approach in order to quantify mobility uncertainty and its evolution with location updates. We discretize the userâs world to obtain a map that we represent as a mobility graph. We model mobility as a random walk on this graph âequivalent to a Markov chain âand quantify trajectory uncertainty as the entropy of the distribution over possible trajectories. In this setting, a location update amounts to conditioning on a particular state of the Markov chain, which requires the computation of the entropy of conditional Markov trajectories. Our main result enables us to compute this entropy through a transformation of the original Markov chain. We apply our framework to real-world mobility datasets and show that the influence of intermediate locations on trajectory entropy depends on the nature of these locations. We build on this finding and design a segmentation algorithm that uncovers intermediate destinations along a trajectory. The final perspective from which we analyze mobility is the data mining perspective: we go beyond simple mobility and analyze geo-tagged data that is generated by online social medias and that describes the whole user experience. We postulate that mining geo-tagged data enables us to obtain a rich representation of the user experience and all that surrounds its mobility. We propose a hierarchical probabilistic model that enables us to uncover specific descriptions of geographical regions, by analyzing the geo-tagged content generated by online social medias. By applying our method to a dataset of 8 million geo-tagged photos, we are able to associate with each neighborhood the tags that describe it specifically, and to find the most unique neighborhoods in a city