The goal of this project is to enable greater sharing of crowd-sensed data, while achieving provable privacy guarantees. Our approach is to limit knowledge of the location traces of crowd-sensed data to exclude the origin and destination, and then consider the system using techniques from distributed control and differential privacy: this enables exploration of the theoretical bounds on the cost of privacy. The achieved results will create a new approach for building and analyzing networked cyber-physical systems (CPS) that permits users to make decisions on crowd-sensed data, and at the same time protects their privacy in a rigorous sense. The project has focused outreach activities in developing a software environment for students to explore privacy-performance trade-offs in transportation operations and an advanced course on security and privacy of CPS. In our technical approach, we focus on the privacy of user inputs, such as the origin (initial state), destination (preference), and utility functions. This enables us to model and analyze how the crowd-sensed data can be used to infer these sensitive inputs, using techniques adapted from the fields of distributed control and differential privacy. The various notions of privacy will support a broad research program, including performance limits of private network control, design principles for different classes of cyber-physical systems, and new location privacy metrics that take into account location popularity. These contributions advance the field of formal analysis of probabilistic models, and the burgeoning subfield of privacy in control and optimization. The theoretical research will be motivated by and evaluated on simulations and proof-of-concept implementations in the context of crowd-sourced congestion detection.