Gauri Vakde

Graduation Semester and Year

2009

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

Matthew Wright

Abstract

Opportunistic Networking holds a great deal of potential for making communications easier and more flexible in pervasive assistive environments. However, security and privacy must be addressed to make these communications acceptable with respect to protecting patient privacy. We propose Privacy-Enhanced Opportunistic Networking (PEON), a system for using opportunistic networking in privacy-preserving way. PEON uses concepts from anonymous communications, re-routing messages through groups of peer nodes to hide the relation between the sources and destinations. We describe a set of protocols that explore a practical range of trade-offs between privacy and communication costs by modifying how closely the protocol adheres to the optimal predicted path. We also present the results of extensive trace-based simulation experiments that allow us to both compare between our proposed protocols and observe the costs of increasing the number of groups and intermediate nodes in a path.

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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