Differential Privacy of Populations in Routing Games
This addresses privacy concerns for individuals in transportation systems where spatiotemporal data could reveal personal activities, though it appears incremental as it builds on existing stochastic online learning frameworks.
The paper tackles the problem of preserving privacy in routing games by analyzing the differential privacy of traffic flow measurements from origin-destination data, providing theoretical guarantees on convergence rates and privacy values with simulation validation.
As our ground transportation infrastructure modernizes, the large amount of data being measured, transmitted, and stored motivates an analysis of the privacy aspect of these emerging cyber-physical technologies. In this paper, we consider privacy in the routing game, where the origins and destinations of drivers are considered private. This is motivated by the fact that this spatiotemporal information can easily be used as the basis for inferences for a person's activities. More specifically, we consider the differential privacy of the mapping from the amount of flow for each origin-destination pair to the traffic flow measurements on each link of a traffic network. We use a stochastic online learning framework for the population dynamics, which is known to converge to the Nash equilibrium of the routing game. We analyze the sensitivity of this process and provide theoretical guarantees on the convergence rates as well as differential privacy values for these models. We confirm these with simulations on a small example.