Key Management System for Private Car-Sharing Scenarios

Hernández, Ana Cecilia Chumaceiro; Roca, Jordi Castella; Viejo, Alexandre

doi:10.1109/vtcfall.2018.8690839

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…Thus, it allows the SP to collect the information exchanged between the vehicle, SP, and each of the users for every vehicle access provision. Hernandez et al [81] proposed a secure key management system enabling vehicle sharing, utilizing a central key and a distributed management server with PKI infrastructure. Groza et al [82] focused on the use of HSMs utilizing identity-based signatures aiming at providing and delegating access to a vehicle.…”

Section: Related Workmentioning

confidence: 99%

HERMES: Scalable, Secure, and Privacy-Enhancing Vehicular Sharing-Access System

Symeonidis

Rotaru²,

Mustafa

et al. 2022

IEEE Internet Things J.

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We propose HERMES, a scalable, secure, and privacy-enhancing system for users to share and access vehicles. HERMES securely outsources operations of vehicle access token (AT) generation to a set of untrusted servers. It builds on an earlier proposal, namely, SePCAR, and extends the system design for improved efficiency and scalability. To cater to system and user needs for secure and private computations, HERMES utilizes and combines several cryptographic primitives with secure multiparty computation (MPC) efficiently. It conceals secret keys of vehicles and transaction details from the servers, including vehicle booking details, AT information, and user and vehicle identities. It also provides user accountability in case of disputes. Besides, we provide semantic security analysis and prove that HERMES meets its security and privacy requirements. Last but not least, we demonstrate that HERMES is efficient and, in contrast to SePCAR, scales to a large number of users and vehicles, making it practical for real-world deployments. We build our evaluations with two different MPC protocols: 1) HtMAC-MiMC and 2) CBC-MAC-AES. Our results demonstrate that HERMES is in the range of milliseconds for generating an AT, whether it operates for a single-vehicle owner or a large rental-company branch with over 1000 vehicles; handling 546 and 84 AT generations per second, respectively. As a result, HERMES is an order of magnitude faster compared to SePCAR. Specifically, it delivers 696 (with HtMAC-MiMC) and 42 (with CBC-MAC-AES) more ATs compared to in SePCAR for a single-vehicle owner AT generation. Furthermore, we show that HERMES is practical on the vehicle side, too, as AT operations performed on a prototype vehicle on-board unit take only ≈ 62 ms.

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Section: Related Workmentioning

confidence: 99%

HERMES: Scalable, Secure, and Privacy-Enhancing Vehicular Sharing-Access System

Symeonidis

Rotaru²,

Mustafa

et al. 2022

IEEE Internet Things J.

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“…However, the use of ML for achieving confidentiality is not much studied in the literature. Most of the works in the literature are about the use of keys to secure data and achieve confidentiality within a system [99]- [102].…”

Section: Security Attacks and Requirementsmentioning

confidence: 99%

“…In the context of privacy, the use of ML is at an infant stage and is receiving attention recently. The use of ML for achieving confidentiality is not much studied in the literature, and it appears that most of the work use key-based approaches which seem enough to achieve confidentiality within in a system [99]- [102].…”

Section: Observations and Lessons Learnedmentioning

confidence: 99%

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Talpur,

Gurusamy

2021

Preprint

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Machine Learning (ML) has emerged as an attractive and viable technique to provide effective solutions for a wide range of application domains. An important application domain is vehicular networks wherein ML-based approaches are found to be very useful to address various problems. The use of wireless communication between vehicular nodes and/or infrastructure makes it vulnerable to different types of attacks. In this regard, ML and its variants are gaining popularity to detect attacks and deal with different kinds of security issues in vehicular communication. In this paper, we present a comprehensive survey of ML-based techniques for different security issues in vehicular networks. We first briefly introduce the basics of vehicular networks and different types of communications. Apart from the traditional vehicular networks, we also consider modern vehicular network architectures. We propose a taxonomy of security attacks in vehicular networks and discuss various security challenges and requirements. We classify the ML techniques developed in the literature according to their use in vehicular network applications. We explain the solution approaches and working principles of these ML techniques in addressing various security challenges and provide insightful discussion. The limitations and challenges in using ML-based methods in vehicular networks are discussed. Finally, we present observations and lessons learned before we conclude our work.

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Privacy-Preserving Identity Management in Car Sharing

Huang,

Shen

2024

Wireless Networks

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Key Management System for Private Car-Sharing Scenarios

Cited by 8 publications

References 5 publications

HERMES: Scalable, Secure, and Privacy-Enhancing Vehicular Sharing-Access System

HERMES: Scalable, Secure, and Privacy-Enhancing Vehicular Sharing-Access System

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Privacy-Preserving Identity Management in Car Sharing

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