Graduation Semester and Year




Document Type


Degree Name

Master of Science in Computer Science


Computer Science and Engineering

First Advisor

Donggang Liu


While many protocols for sensor network security provide con dentiality for the content of messages, contextual information usually remains exposed. Such contextual information can be exploited by an adversary to derive sensitive information such as the locations of monitored objects and data sinks in the eld. Attacks on these components can signi cantly undermine network applications. The existing techniques defend the leakage of location information only from an adversary who sees only local network tra c. However, a stronger adversary, the global eavesdrop- per, is realistic and can defeat all existing techniques. This paper rst formalizes the location privacy issues in sensor networks under this strong adversary model and computes a lower bound on the communication overhead needed for achieving a certain level of location privacy. The paper then proposes two techniques to provide location privacy for monitored objects (source location privacy): periodic collection and source simulation, and two techniques to provide location privacy for data sinks (destination location privacy): destination simulation and backbone ooding. These techniques provide trade-o s between privacy, communication cost, and latency. The analysis and simulation demonstrate that the proposed techniques are e cient and e ective for source and destination location privacy in sensor networks.


Computer Sciences | Physical Sciences and Mathematics


Degree granted by The University of Texas at Arlington