Today, operators of cellular networks and electricity grids measure large volumes of data, which can provide rich insights into city-wide mobility and congestion patterns. Sharing such real-time societal trends with independent, external entities, such as a taxi fleet operator, can enhance city-scale resource allocation and control tasks, such as electric taxi routing and battery storage optimization. However, the owner of a rich time series and an external control authority must communicate across a data boundary, which limits the scope and volume of data they can share. This project will develop novel algorithms and systems to jointly compress, anonymize, and price rich time series data in a way that only shares minimal, task-relevant data across organizational boundaries. By emphasizing communication efficiency, the developed algorithms will incentivize data sharing and collaboration in future smart cities.