Physical Sciences and Mathematics Commons^™

Routing In The Dark: Pitch Black, Nathan S. Evans

Electronic Theses and Dissertations

In many networks, such as mobile ad-hoc networks and friend-to-friend overlay networks, direct communication between nodes is limited to specific neighbors. Friendto-friend “darknet” networks have been shown to commonly have a small-world topology; while short paths exist between any pair of nodes in small-world networks, it is non-trivial to determine such paths with a distributed algorithm. Recently, Clarke and Sandberg proposed the first decentralized routing algorithm that achieves efficient routing in such small-world networks.

Herein this thesis we discuss the first independent security analysis of Clarke and Sandberg’s routing algorithm. We show that a relatively weak participating adversary can render …

Generalized Neuron Based Secure Media Access Control Protocol For Wireless Sensor Networks, Raghavendra V. Kulkarni, Ganesh K. Venayagamoorthy, Abhishek V. Thakur, Sanjay Kumar Madria

Electrical and Computer Engineering Faculty Research & Creative Works

Security plays a pivotal role in most applications of wireless sensor networks. It is common to find inadequately secure networks confined only to controlled environments. The issue of security in wireless sensor networks is a hot research topic for over a decade. This paper presents a compact generalized neuron (GN) based medium access protocol that renders a CSMA/CD network secure against denial-of-service attacks launched by adversaries. The GN enhances the security by constantly monitoring multiple parameters that reflect the possibility that an attack is launched by an adversary. Particle swarm optimization, a popular bio-inspired evolutionary-like optimization algorithm is used for …

Beyond K-Anonymity: A Decision Theoretic Framework For Assessing Privacy Risk, Guy Lebanon, Monica Scannapieco, Mohamed Fouad, Elisa Bertino

Cyber Center Publications

An important issue any organization or individual has to face when managing data containing sensitive information, is the risk that can be incurred when releasing such data. Even though data may be sanitized before being released, it is still possible for an adversary to reconstruct the original data using additional information thus resulting in privacy violations. To date, however, a systematic approach to quantify such risks is not available. In this paper we develop a framework, based on statistical decision theory, that assesses the relationship between the disclosed data and the resulting privacy risk. We model the problem of deciding …