Map-aided Dead-reckoning --- A Study on Locational Privacy in Insurance Telematics

TL;DR

This paper introduces a particle-based map-aided dead-reckoning method for vehicle localization using speed measurements, revealing privacy risks in insurance telematics data with potential for destination estimation within 100 meters.

Contribution

The study develops a novel particle filter framework leveraging map data and vehicle dynamics to estimate locations, highlighting privacy vulnerabilities in telematics data collection.

Findings

End destinations can be estimated within approximately 100 meters.

The method demonstrates high sensitivity to collected telematics data.

Privacy implications for millions of policyholders are significant.

Abstract

We present a particle-based framework for estimating the position of a vehicle using map information and measurements of speed. Two measurement functions are considered. The first is based on the assumption that the lateral force on the vehicle does not exceed critical limits derived from physical constraints. The second is based on the assumption that the driver approaches a target speed derived from the speed limits along the upcoming trajectory. Performance evaluations of the proposed method indicate that end destinations often can be estimated with an accuracy in the order of $100\,[m]$. These results expose the sensitivity and commercial value of data collected in many of today's insurance telematics programs, and thereby have privacy implications for millions of policyholders. We end by discussing the strengths and weaknesses of different methods for anonymization and privacy preservation in telematics programs.