PRIVACY-PRESERVING TRANSPORTATION TRAFFIC MEASUREMENT IN INTELLIGENT CYBER-PHYSICAL ROAD SYSTEMS By YIAN ZHOU A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy PRIVACY-PRESERVING TRANSPORTATION TRAFFIC MEASUREMENT IN INTELLIGENT CYBER-PHYSICAL ROAD SYSTEMS By Yian Zhou December 2015 Chair: Shigang Chen Major: Computer Engineering Traffic volume measurement is critical in transportation engineering and vehicular networks. Existing research on traffic volume measurement mainly focuses on single-point traffic statistics. In this dissertation, we switch our view from single-point to multi-point, and study the important problem of privacy-preserving multi-point traffic volume measurement in intelligent cyber-physical road systems (CPRS), which complements the state of art. We take advantage of the capabilities provided by CPRS to exploit the potential for a fundamental shift in the way how traffic data in support of multi-point traffic volume measurement can be automatically collected. The objective is to allow transportation authorities to automatically collect and efficiently measure the aggregate multi-point traffic volume data from CPRS without learning information about individual vehicles. In this dissertation, we start with the problem of privacy-preserving two-point traffic volume measurement in CPRS, and propose four novel measurement schemes to solve this problem, with varying degrees of efficiency, accuracy, and privacy. Our first two schemes protect vehicles’ identities through keyed signatures based on a family of commutative one-way hash functions, and they can achieve exact measurement results. The third and fourth schemes achieve better privacy for vehicles through shared bit array masking, protecting vehicles’ identities as well as their travelling trajectory. They are also much more efficient, and can gracefully control the tradeoff between vehicles’ privacy and measurement accuracy. In particular, our third scheme utilizes fixed-length bit arrays, and it works great under the