Differential Privacy of Populations in Routing Games

TL;DR

This paper investigates how to ensure differential privacy in routing games by analyzing the privacy of origin-destination flows and traffic measurements, providing theoretical guarantees and simulations for privacy-preserving population dynamics.

Contribution

It introduces a stochastic online learning framework for routing games that guarantees differential privacy while converging to Nash equilibrium.

Findings

The framework achieves differential privacy with provable guarantees.

Convergence rates to Nash equilibrium are established.

Simulations confirm theoretical privacy and convergence results.

Abstract

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.