Privacy Protection of Automotive Location Data Based on Format-Preserving Encryption of Geographical Coordinates

TL;DR

This paper introduces a format-preserving encryption method for automotive geographical coordinates that significantly enhances privacy protection while maintaining data accuracy for autonomous driving applications.

Contribution

It proposes a novel high-precision encryption mechanism based on FPE that reduces accuracy loss and effectively prevents location-based privacy attacks.

Findings

Average RDR of 0.0844 indicating high retention of distance information

Hotspot areas decreased by 98.9% after encryption

Decrypted coordinates achieve 100% restoration accuracy

Abstract

There are increasing risks of privacy disclosure when sharing the automotive location data in particular functions such as route navigation, driving monitoring and vehicle scheduling. These risks could lead to the attacks including user behavior recognition, sensitive location inference and trajectory reconstruction. In order to mitigate the data security risk caused by the automotive location sharing, this paper proposes a high-precision privacy protection mechanism based on format-preserving encryption (FPE) of geographical coordinates. The automotive coordinate data key mapping mechanism is designed to reduce to the accuracy loss of the geographical location data caused by the repeated encryption and decryption. The experimental results demonstrate that the average relative distance retention rate (RDR) reached 0.0844, and the number of hotspots in the critical area decreased by 98.9% after encryption. To evaluate the accuracy loss of the proposed encryption algorithm on automotive geographical location data, this paper presents the experimental analysis of decryption accuracy, and the result indicates that the decrypted coordinate data achieves a restoration accuracy of 100%. This work presents a high-precision privacy protection method for automotive location data, thereby providing an efficient data security solution for the sensitive data sharing in autonomous driving.