Privacy Preserving Driving Style Recognition

TL;DR

This paper introduces a privacy-preserving method for classifying driving styles as aggressive or defensive using vehicle data, ensuring driver privacy while aiding insurance companies in risk assessment.

Contribution

The paper proposes a novel privacy-preserving technique for driving style recognition that prevents data leakage between vehicles and insurers.

Findings

The method accurately classifies driving styles without compromising privacy.

Experimental results demonstrate the approach's efficiency and effectiveness.

The technique maintains privacy while enabling reliable driving behavior analysis.

Abstract

In order to better manage the premiums and encourage safe driving, many commercial insurance companies (e.g., Geico, Progressive) are providing options for their customers to install sensors on their vehicles which collect individual vehicle's traveling data. The driver's insurance is linked to his/her driving behavior. At the other end, through analyzing the historical traveling data from a large number of vehicles, the insurance company could build a classifier to predict a new driver's driving style: aggressive or defensive. However, collection of such vehicle traveling data explicitly breaches the drivers' personal privacy. To tackle such privacy concerns, this paper presents a privacy-preserving driving style recognition technique to securely predict aggressive and defensive drivers for the insurance company without compromising the privacy of all the participating parties. The insurance company cannot learn any private information from the vehicles, and vice-versa. Finally, the effectiveness and efficiency of the privacy-preserving driving style recognition technique are validated with experimental results.