# Privacy-Preserving Authentication Based on PUF for VANETs
## 1 Introduction  
With the escalating number of vehicles, vehicular ad hoc networks (VANETs) are poised to enhance the quality of travel and traffic conditions [1]. Typically, VANETs are comprised of three primary components: certification authority (CA), vehicles equipped with on-board units (OBUs), and roadside units (RSUs). These networks primarily utilize two principal communication modes: vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I), both communication modes adhere to the dedicated short-range communication protocol for wireless access in the vehicular environment [2, 3]. However, due to the inherent openness of wireless channels within VANETs, adversaries can readily execute a range of attacks, including a denial of service attack (DoS), a Sybil attack, and so on. Hence, in wireless channel communication scenarios, security and privacy-preserving are critical challenges [4, 5]. In particular, traditional security standards such as confidentiality, authentication, and integrity serve to guarantee that transmitted messages are accessible solely to authorized entities, thereby upholding the integrity and security of the communication process [6, 7]. However, most traditional solutions are based on the assumption that the secret key of a vehicle is securely stored in an ideal tamper-proof device (TPD). However, as physical attacks, such as side-channel attacks, become more powerful, an adversary can retrieve the secret key from TPD. In recent years, the physical unclonable function (PUF) has been regarded as a promising tool for protecting against physical attacks [8]. The PUF is easy to evaluate but hard to predict, and it is impossible to replicate. Due to random uncontrollable variables in the manufacturing process, the PUF can generate a challenge-response pair (CRP) that is also unique. Therefore, the PUF has significantly higher physical security by generating unique CRP rather than storing secret keys in memories [9]. In addition, the CRPs need to be updated. In recent developments, route planning has emerged as a prevalent practice within the self-driving vehicle landscape [10]. During the route planning phase, a vehicle strategically chooses its preferred route, subsequently communicating this decision to the RSUs along its trajectory with the assistance of the CA. Upon entering the coverage area of the designated RSUs, swift and highly effective authentication is facilitated between the vehicle and RSUs through the exchange of shared messages [11]. Hence, in order to prevent the secret key from physically being stolen by adversaries and speed up authentication, we proposed a physical-preserving authentication based on PUF for VANETs in this paper, and we summarized the main contributions as follows. * The proposed scheme provides physical security through PUF. In particular, the secret key of the vehicle is generated by a challenge-response mechanism based on PUF, instead of storing the secret key in TPD's physical memory. Therefore, adversaries cannot obtain the secret key. And fuzzy extractor (FE) technology is introduced to enhance the stability of PUF and mitigate various electrical noise interferences. Furthermore, aside from the unlikability of pseudonyms, we also propose an update mechanism for CRPs to ensure that adversaries cannot attack user privacy by analyzing CRPs;
* In the proposed scheme, a vehicle plans its route in advance, knowing which RSUs it will pass by. Then, Before V2I authentication, the vehicle requests the CA for the secret authentication keys of all RSUs on its path at once. The request process introduces oblivious transfer (OT) technology to avoid leaking the vehicle's driving trajectory. Next, the V2I authentication will be sped up when the vehicle enters the RSUs' coverage. As a result, this improves the authentication efficiency when the vehicle is roaming among different RSUs' coverage;
* A comprehensive analysis demonstrates that the proposed scheme effectively fulfills security objectives within VANETs.