The use of Electronic Toll Collection (ETC) systems is on the rise, as these systems have a significant impact on reducing operational costs. Toll service providers (TSPs) access various information, including drivers' IDs and monthly toll fees, to bill drivers. While this is legitimate, such information could be misused for other purposes violating drivers' privacy, most prominent, to infer drivers' movement patterns. To this end, privacy-preserving ETC (PPETC) schemes have been designed to minimize the amount of information leaked while still allowing drivers to be charged. We demonstrate that merely applying such PPETC schemes to current ETC infrastructures may not ensure privacy. This is due to the (inevitable) minimal information leakage, such as monthly toll fees, which can potentially result in a privacy breach when combined with additional background information, such as road maps and statistical data. To show this, we provide a counterexample using the case study of Brisbane's ETC system. We present two attacks: the first, being a variant of the presence disclosure attack, tries to disclose the toll stations visited by a driver during a billing period as well as the frequency of visits. The second, being a stronger attack, aims to discover cycles of toll stations (e.g., the ones passed during a commute from home to work and back) and their frequencies. We evaluate the success rates of our attacks using real parameters and statistics from Brisbane's ETC system. In one scenario, the success rate of our toll station disclosure attack can be as high as 94%. This scenario affects about 61% of drivers. In the same scenario, our cycle disclosure attack can achieve a success rate of 51%. It is remarkable that these high success rates can be achieved by only using minimal information as input, which is, e.g., available to a driver's payment service provider or bank, and by following very simple attack strategies without exploiting optimizations. As a further contribution, we analyze how the choice of various parameters, such as the set of toll rates, the number of toll stations, and the billing period length, impact a driver's privacy level regarding our attacks.

Being part of the 6G ecosystem vision, Connected and Automated Vehicles (CAVs) will enjoy sophisticated tailored services offering road safety and entertainment for users. As one of the 6G cornerstones, Network Slicing (NS) allows the creation of various customized 6G-V2X (Vehicle-to-Everything) use cases on the same physical infrastructure. However, 6G-NS advances can open up breaches to cyber-attacks aiming to break 6G-V2X Network slices to inflict maximum damage on CAVs and their users. Crossing borders, where CAVs leave their V2X-NS (V2X Network Slice) in the Home Mobile Network Operator (H-MNO) toward a similar V2X-NS in the Visited MNO (V-MNO), is an attractive opportunity to exploit by attackers. Detecting and mitigating attacks, in this case, becomes a priority, confronted by NS requirements and MNOs not ready to share their private data. To this end, this paper proposes a 3GPP-compliant privacy preservation collaborative learning scheme for 6G-NS security, focusing on V2X-NS cross-border areas. Our scheme leverages multi-process Federated Learning (FL) architecture to build efficient V2X-NS security-related models while preserving 6G V2X-NS isolation. In addition, it uses differential privacy-enabled stacking to build up attack detection knowledge at the V2X-NSs and MNOs levels while ensuring privacy preservation. We conducted an experimental study on the 5G-NIDD dataset, which is one of the most realistic publicly available 5G datasets. Our results demonstrate that multi-process FL with stacking can deliver high accuracy while ensuring isolation between 6G-V2X-NSs and privacy preservation between H-MNO and V-MNO.

As part of Intelligent Transportation Systems (ITS), Electronic toll collection (ETC) is a type of toll collection system (TCS) which is getting more and more popular as it can not only help to finance the government’s road infrastructure but also it can play a crucial role in pollution reduction and congestion management. As most of the traditional ETC schemes (ETCS) require identifying their users, they enable location tracking. This violates user privacy and poses challenges regarding the compliance of such systems with privacy regulations such as the EU General Data Protection Regulation (GDPR). So far, several privacy-preserving ETC schemes have been proposed. To the best of our knowledge, this is the first survey that systematically reviews and compares various characteristics of these schemes, including components, technologies, security properties, privacy properties, and attacks on ETCS. This survey first categorizes the ETCS based on two technologies, GNSS and DSRC. Then under these categories, the schemes are classified based on whether they provide formal proof of security and support security analysis. We also demonstrate which schemes specifically are/are not resistant to collusion and physical attacks. Then, based on these classifications, several limitations and shortcomings in privacy-preserving ETCS are revealed. Finally, we identify several directions for future research.