Differential Privacy Approaches in Telematics-Based Auto Insurance Systems

The rapid integration of telematics in the automotive insurance industry has enabled Usage-Based Insurance (UBI) models that reward safe driving behavior. However, the high-resolution GPS and accelerometer data collected from vehicles pose significant privacy risks, as they can reveal sensitive information about a policyholder’s lifestyle, home location, and routines. This research explores the application of Differential Privacy (DP) —a mathematical framework for quantifying and limiting privacy loss—within telematics ecosystems. By injecting controlled statistical noise into driving datasets, this study evaluates the trade-off between privacy protection and the actuarial accuracy of risk scoring models. The findings indicate that while DP significantly mitigates reidentification risks, high levels of privacy (low epsilon values) can degrade the predictive power of acceleration-based risk metrics. A hybrid DP approach is proposed to balance these competing interests.