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DECLARATION OF ORIGINALITY I hereby certify that I am the sole author of this thesis and that no part of this thesis has been published or submitted for publication. I certify that, to the best of my knowledge, my thesis does not infringe upon anyone's copyright nor violate any proprietary rights and that any ideas, techniques, quotations, or any other material from the work of other people included in my thesis, published or otherwise, are fully acknowledged in accordance with the standard referencing practices. Furthermore, to the extent that I have included copyrighted material that surpasses the bounds of fair dealing within the meaning of the Canada Copyright Act, I certify that I have obtained a written permission from the copyright owner(s) to include such material(s) in my thesis and have included copies of such copyright clearances to my appendix. I declare that this is a true copy of my thesis, including any final revisions, as approved by my thesis committee and the Graduate Studies office, and that this thesis has not been

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Safety applications in Vehicular Ad-hoc Networks (VANETs) often require vehicles to share information such as current position, speed, and vehicle status on a regular basis. This information can be collected to obtain private information about vehicles/drivers, such as home or office locations and frequently visited places, creating serious privacy vulnerabilities. The use of pseudonyms, rather than actual vehicle IDs, can alleviate this problem and several different Pseudonym Management Techniques (PMTs) have been proposed in the literature. These PMTs are typically evaluated assuming a random placement of attacking stations. However, an adversary can utilize knowledge of traffic patterns and PMTs to place eavesdropping stations in a more targeted manner, leading to an increased tracking success rate. In this paper, we propose two new adversary placement strategies and study the impact of intelligent adversary placement on tracking success using different PMTs. The results indicate that targeted placement of attacking stations, based on traffic patterns, road type, and knowledge of PMT used, can significantly increase tracking success. Therefore, it is important to take this into consideration when developing PMTs that can protect vehicle privacy even in the presence of targeted placement techniques.

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