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

• URL: http://arxiv.org/abs/2510.20300v1
• Date: Thu, 23 Oct 2025 07:39:59 GMT
• Title: Privacy Protection of Automotive Location Data Based on Format-Preserving Encryption of Geographical Coordinates
• Authors: Haojie Ji, Long Jin, Haowen Li, Chongshi Xin, Te Hu,
• Abstract summary: This paper proposes a high-precision privacy protection mechanism based on format-preserving encryption (FPE) of geographical coordinates.<n>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.
• Score: 7.607399833486961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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