Transportation networks are part of our critical infrastructure. Location data from vehicles on these networks is important for making infrastructure policies, studying congestion, reducing pollution, real time routing, and providing value added services (e.g., Uber, Lyft). However, this location data could lead to inferring information that users might consider private such as their residential locations, health and economic status, and religious affiliation. Obfuscation mechanisms have been proposed as effective ways to protect the privacy of individual users while sharing their locations. However, users have limited control over the amount of privacy on their data as they have no easy way to interact with obfuscation mechanisms which are either too rigid (with their parameters) or too naive (with their assumptions) to be truly effective. In this seed project, we propose to develop a policy-based framework for adaptive location obfuscation. Our framework will provide users with strong privacy guarantees while effectively allowing them to balance the tradeoff between utility and privacy, depending upon their needs. Thus, it will improve security and privacy of data sharing in services and critical infrastructure such as traffic flow networks.