# Privacy-Preserving Vehicle Assignment for Mobility-on-Demand Systems

## TL;DR

This paper introduces a privacy-preserving vehicle assignment method for mobility-on-demand systems that protects passenger destinations while maintaining efficient service, using formal privacy guarantees and a scalable algorithm tested on real-world data.

## Contribution

It formalizes privacy in vehicle-passenger assignment, integrates a privacy mechanism with guarantees, and develops a scalable algorithm leveraging redundant vehicles to reduce performance loss.

## Key findings

- Privacy can be integrated without significant performance loss.
- Redundant vehicles help mitigate privacy-induced performance deterioration.
- Method is effective on large-scale real-world taxi data.

## Abstract

Urban transportation is being transformed by mobility-on-demand (MoD) systems. One of the goals of MoD systems is to provide personalized transportation services to passengers. This process is facilitated by a centralized operator that coordinates the assignment of vehicles to individual passengers, based on location data. However, current approaches assume that accurate positioning information for passengers and vehicles is readily available. This assumption raises privacy concerns. In this work, we address this issue by proposing a method that protects passengers' drop-off locations (i.e., their travel destinations). Formally, we solve a batch assignment problem that routes vehicles at obfuscated origin locations to passenger locations (since origin locations correspond to previous drop-off locations), such that the mean waiting time is minimized. Our main contributions are two-fold. First, we formalize the notion of privacy for continuous vehicle-to-passenger assignment in MoD systems, and integrate a privacy mechanism that provides formal guarantees. Second, we present a scalable algorithm that takes advantage of superfluous (idle) vehicles in the system, combining multiple iterations of the Hungarian algorithm to allocate a redundant number of vehicles to a single passenger. As a result, we are able to reduce the performance deterioration induced by the privacy mechanism. We evaluate our methods on a real, large-scale data set consisting of over 11 million taxi rides (specifying vehicle availability and passenger requests), recorded over a month's duration, in the area of Manhattan, New York. Our work demonstrates that privacy can be integrated into MoD systems without incurring a significant loss of performance, and moreover, that this loss can be further minimized at the cost of deploying additional (redundant) vehicles into the fleet.