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.

The key motivation of this work is that today's representations of time series data are designed independently of an ultimate control task, which often causes unnecessary temporal features to be sent, private features to be revealed, and the most salient trends to be under-valued. Accordingly, this project will develop a unified approach to co-design succinct, private representations of rich time series data along with an ultimate control task. Here, co-design means that the forecast representation is learned within the broader context of a control objective while accounting for bandwidth constraints, privacy, and economic costs and incentives for data processing. The algorithms will compute a controller's sensitivity to prediction errors, which can arise from data compression, forecast uncertainty, as well as artificial noise injected by modern privacy tools. Crucially, the controller's sensitivity will in turn be relayed to a network operator to guide its optimization and learning (e.g., co-design) of a concise, task-relevant forecast representation that masks private attributes and naturally prices temporal features by their importance to control. The research will, for example, enable operators to flexibly use the same underlying cell demand data to emphasize peak-hour variability for taxi routing, while seamlessly delivering fine-grained throughput forecasts to a mobile video streaming company without revealing private user mobility. Finally, the case studies in this project will be integrated into courses on learning-based control at UT Austin and Cornell. Broader impacts also include outreach and inclusion efforts to engage students from groups that have historically been under-represented in STEM fields.