Business Value of Privacy-Preserving Mobility Analytics in Platform Operations: A Federated Risk-Utility Framework

Abstract

Mobility platforms depend on continuous analytics of location traces, travel time, pickup density, delivery reliability, and user behavior. These data create operational value, but they also expose platforms to privacy, regulatory, communication, and trust risks when raw trajectories are centralized. This article develops a federated risk-utility framework for evaluating the business value of privacy-preserving mobility analytics in platform operations. The framework compares five analytics architectures: centralized raw-data analytics, local-only analytics, basic federated learning, differentially private federated learning, and a governed federated design that combines privacy controls, secure aggregation, compression, and managerial value gates. A numerical scenario analysis is used to examine prediction utility, privacy exposure, communication cost, expected regulatory loss, customer trust, and risk-adjusted operating value across ride-hailing, delivery, courier, and shared-mobility settings. Results show that the highest predictive utility does not necessarily create the highest business value. The governed federated risk-utility architecture produces the strongest risk-adjusted operating value because it preserves most of the operational benefit of mobility analytics while substantially reducing privacy exposure and compliance loss. The study contributes a business analytics perspective on federated mobility intelligence and provides actionable governance thresholds for platform managers.

Keywords: Mobility analytics; platform operations; federated learning; privacy preserving analytics; risk utility framework; business value; urban trajectory mining; data governance