Authors:
D. Palma, P.L. Montessoro, M. Loghi, and D. Casagrande
Date:
2025
Publisher:
Wiley Online Library
Journal:
Advanced Intelligent Systems
Cite:
D. Palma, P.L. Montessoro, M. Loghi, and D. Casagrande, "A Privacy-Preserving System for Confidential Carpooling Services Using Homomorphic Encryption," in Advanced Intelligent Systems (early access), 2025.
Bibtex:
@article{PM2024Access,
author = {Palma, David and Montessoro, Pier Luca and Loghi, Mirko and Casagrande, Daniele},
journal = {Advanced Intelligent Systems},
pages = {2400507},
publisher = {Wiley Online Library},
title = {A Privacy-Preserving System for Confidential Carpooling Services Using Homomorphic Encryption},
year = {2025},
}Abstract:
Carpooling enables multiple users with similar travel habits to share rides, reducing vehicles on the road, leading to benefits such as lower fuel consumption, reduced traffic congestion, and lower environmental impact. However, carpooling also poses a challenge to the privacy of the users, as they may not want to reveal their location or route information to others. This research study delves into a cutting-edge approach to address these privacy concerns by leveraging homomorphic encryption (HE) within the realm of carpooling services. The proposed solution makes use of a HE scheme that supports encrypted computation on real numbers, which is suitable for carpooling applications that involve distance and time calculations. The approach enables decision makers to perform efficient and accurate route matching over encrypted data, without disclosing their sensitive information about users, thus preserving the confidentiality of the data. The proposed system is evaluated through extensive experiments and simulations, demonstrating its effectiveness in terms of both security and privacy when the system operates in normal (ideal) and abnormal (under attack) conditions. Experimental results indicate that the proposed solution offers robust resistance to various attacks, including replay attacks and data exposure, providing a robust and privacy-centric solution for carpooling services.