https://orcid.org/0000-0002-2585-9220
Figures
Abstract
The study of security and privacy in vehicular ad hoc networks (VANETs) has become a hot topic that is wide open to discussion. As the quintessence of this aspect, authentication schemes deployed in VANETs play a substantial role in providing secure communication among vehicles and the surrounding infrastructures. Many researchers have proposed a variety of schemes related to information verification and computation efficiency in VANETs. In 2018, Kazemi et al. proposed an evaluation and improvement work towards Azees et al.’s efficient anonymous authentication with conditional privacy-preserving (EAAP) scheme for VANETs. They claimed that the EAAP suffered from replaying attacks, impersonation attacks, modification attacks, and cannot provide unlinkability. However, we also found out if Kazemi et al.’s scheme suffered from the unlinkability issue that leads to a forgery attack. An adversary can link two or more messages sent by the same user by applying Euclid’s algorithm and derives the user’s authentication key. To remedy the issue, in this paper, we proposed an improvement by encrypting the message using a shared secret key between sender and receiver and apply a Nonce in the final message to guarantee the unlinkability between disseminated messages.
Citation: Cahyadi EF, Hwang M-S (2021) An improved efficient anonymous authentication with conditional privacy-preserving scheme for VANETs. PLoS ONE 16(9): e0257044. https://doi.org/10.1371/journal.pone.0257044
Editor: Muhammad Khurram Khan, King Saud University, SAUDI ARABIA
Received: June 11, 2021; Accepted: August 21, 2021; Published: September 10, 2021
Copyright: © 2021 Cahyadi, Hwang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The research is in theorem analysis rather than empirical analysis. All information necessary to replicate the findings of the study can be found in the manuscript.
Funding: The Ministry of Science and Technology partially supports this research, Taiwan (ROC), under contract no.: MOST 109-2221-E-468-011-MY3. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study.
Competing interests: The authors have declared that no competing interests exist.
Introduction
VANETs are loaded with intelligent transportation system (ITS) properties, which make all vehicles on the road could communicate with each other via vehicle-to-vehicle (V2V) communications and to infrastructure alongside the road by vehicle-to-infrastructure (V2I) communications [1–5]. It comprises three primary entities, i.e., trusted authority (TA), roadside unit (RSU), and the on-board unit (OBU) (see Fig 1). TA acts as the trust and security management center of the entire VANETs entities. Its job includes registration and parameters generation for RSUs and OBUs after joining the networks [6–9]. Meanwhile, RSUs are semi-trusted fixed infrastructures located along the road at dedicated locations and fully controlled by TA [10]. They act as a bridge between TA and vehicles (OBUs). An OBU is equipped in every vehicle as a transceiver unit. It could broadcast a traffic-related message such as position, speed, and direction, to hundreds of other vehicles or RSUs every 100-300 ms [11].
One of the security aspects of VANET is against its malicious software [12–15]. The attacker accesses the vehicle’s network through wireless communication and uses malicious viruses to conduct malicious attacks. These malicious viruses interfere with normal vehicle communication, deceive or tamper with information, and will seriously threaten the security of the Internet of Vehicles [12, 14].
The security and privacy aspects of the information dissemination process in VANETs strongly rely on its authentication scheme. Some popular technology, such as cloud computing, improves efficiency with its abundant storage and computing resources [16, 17]. The OBUs that storing private information are generally required to verify about 1000-5000 messages per second with about 100-500 vehicles in their communication range. At this condition, cloud-assisted VANET has greatly benefited OBUs to cope with the heavy computational tasks loading and improving road safety and traffic efficiency [18]. However, cloud providers normally use relational databases for storing metadata, which is vulnerable to being violated from users’ data privacy point of view. Authors in [19] proposed a framework based on database schema redesign and dynamic reconstruction of metadata to ensure that the privacy of cloud users’ data does not get compromised by an adversary even if they succeed in gaining access to the associated metadata.
Encrypting user information does not guarantee the security of user privacy, and the query itself may reveal the user’s location information and identity [20, 21]. Aiming at the privacy and security issues of location-based services in the Internet of Vehicles environment. Xie et al. proposed a PPA-IOV privacy protection algorithm based on the analysis of LBS privacy security technology, combined with K area and pseudonym anonymity technology [21].
To enhance security, many researchers also have included biometrics, such as fingerprints and face patterns, to makes it difficult for an adversary to forge the legitimation of the user [22]. Moreover, its attributes have various desirable properties regarding personal authentication, including dependability, convenience, universality, etc. These characteristics have made biometrics adaptable to general use in authentication systems [23]. Based on the employed cryptographic mechanism, Lu et al. [6] distinguished the privacy-preserving authentication scheme of VANETs into five categories, including public key infrastructure (PKI)-based [11, 24–28], symmetric cryptography-based [29–31], identity (ID)-based signature [32–37], certificateless signature (CLS)-based [38, 39], and group signature-based [8, 9, 40].
In the PKI-based, the main property is each user uses a pair of cryptographic keys: a public and a private key, where the mechanism itself strongly relies on the computational complicacy for private key generation from its corresponding public key. In 2007, Raya-Hubaux [11] proposed a PKI-based CPPA scheme, which used a pair of public-private keys and corresponding certificates to hide a vehicle’s real identity. However, this approach has two main drawbacks: a significant verification overhead and large storage requirements on OBU’s side and a large certification revocation list (CRL) generation that making the revocation process ineffective on TA’s side. In 2008, Lu et al. [24] proposed an efficient conditional privacy preservation (ECPP) protocol to rectify the CRL and the storage space limitations issue, where the vehicle depends on the RSU to obtain a short-term pseudonym. In the same year, Zhang et al. [25] also proposed an efficient RSU-aided message authentication (RAISE) protocol based on a k-anonymity and a hash message authentication code. In 2016, Rajput et al. [26] suggested a privacy-preserving hierarchical pseudonymous-based authentication protocol to resolve these PKI-based drawbacks. This protocol avoids CRL management by presenting hierarchical pseudonyms that differentiate one user from another regarding their time to live.
In 2017, Azees et al. [27] proposed a PKI-based efficient anonymous authentication scheme with a conditional privacy-preserving (EAAP) scheme for VANETs. Thus, the vehicles and RSUs can generate their anonymous certificates to provide privacy, and TA doesn’t require to store them. Meanwhile, in case of dispute, TA can revoke a misbehaving vehicle’s anonymity and disclose its real identity. As a result, the scheme was declared secure against impersonation attacks, bogus message attacks, message modification attacks, and providing privacy preservation and anonymity during the authentication of vehicles and RSUs. However, in 2018, Kazemi et al. [28] published an article that pointed out some weaknesses in the EAAP. First, they claimed if the scheme is vulnerable to replay attacks. The final message sent by the authorized users is not changed until the vehicle’s public key is updated, so the message is not fresh and can be used several times by the adversary . The scheme also claimed to suffer from message modification and impersonation attacks because there are possibilities for
to modify the message’s content or generate fake verifiable messages and generate a valid message on behalf of an authorized vehicle, respectively. Lastly, the scheme cannot provide unlinkability since the authentication key (AK) is not changed. This condition causing a part of the generated challenger is also fixed in different transmissions. In this way,
can track the sender and find out its location in the network. Therefore, Kazemi et al. proposed an improvement work towards the EAAP scheme.
Unfortunately, in this article, we prove if Kazemi et al.’s [28] scheme is also cannot provide unlinkability, which leads to forgery and impersonation attacks. By applying Euclid’s algorithm, an adversary can link two or more messages sent by the same user ui. By deriving the user’s dummy identity
and their partial private key
,
is able to generate the authentication key
of ui. Hence, by encrypting the message M using shared secret key Esk between sender and receiver, and applying a Nonce instead of a timestamp T in the final message msg, our improved scheme can provide unlinkability and so withstand the forgery attacks. The outline of our main contributions are as follows:
- We point out some security flaws in Kazemi et al.’s scheme
- We propose an improvement to address the weaknesses in Kazemi et al.’s scheme
- We demonstrate that our improvement is secure against forgery and impersonation attacks, and can provide unlinkability.
For a better understanding, the rest of this paper is arranged as follows. First, in Section 2, we provide preliminaries. Then, a brief review of Kazemi et al.’s scheme and its cryptanalysis are described in Section 3. Next, in Section 4, we propose our improvement to Kazemi et al.’s scheme, then analyze it in Section 5. Finally, the conclusion is conveyed in Section 6.
Preliminaries
This section introduces the system design, adversary model, and security-privacy requirements that have to be fulfilled in VANETs.
System model
The two-layer concept in VANETs, with TA on the top, while RSUs and OBUs on the lower layer, as seen in Fig 1, have been introduced by [32], and then used by several works [33, 34, 36] since then. The task and function of each entity have been briefly described in Section 1. For advance, the OBU in the vehicle will have communication sensors, tamper-proof device (TPD), DSRC communication medium, event data recorder (EDR), smart card and fingerprint devices, and human-machine interface to calculate an effective decision movement, as shown in Fig 2 [22]. In our VANETs ecosystem, we assume:
- TA is uncompromised
- Only TA that can reveal the real identity of RSUs and OBUs
- TA—RSU communicate through a secured wireline networks
- RSUs are semi-trusted
- TPD is assumed to be credible.
Adversary model
We assume that all RSUs and OBUs in the networks are not fully trusted, and the open wireless communication channel is naturally not secure. In [28], an adversary is capable of performing the following actions:
- Upon receiving two or more messages from the same user ui, an
can relate the encrypted dummy identities
and encrypted OBU’s private keys
to obtain the authentication key
of ui.
- An
may forge the authentication key
of ui and impersonate it when entering a new TA’s region.
Security requirements
To design a secure authentication mechanism, first, we need to define the security and privacy requirements that must meet in VANETs.
- Message authentication: The networks could ensure that data is sent and signed by a legitimate vehicle without being modified in transit.
- Identity privacy-preserving: The identity of the messages’ sender should be anonymous, and only TA can reveal their real identity.
- Traceability: TA capable of revealing the real identity of the users’ pseudo-identity in the case of a dispute.
- Unlinkability: An adversary (vehicle or RSU) should not link two or more subsequent pseudonym messages of the same vehicle.
- Resistance to impersonation attack: The networks could endure towards the attacker trying to assume or impersonate the identity of the legitimate vehicles in VANETs, to generate the signature for any messages.
- Resistance to replaying attack: The networks could endure a passive data capture and subsequent retransmission to produce an unauthorized message by the adversaries.
Weakness on Kazemi et al.’s scheme
In this section, we briefly review Kazemi et al.’s [28] scheme and its cryptanalysis. To comprehend the scheme’s procedure, notations throughout this paper are presented in Table 1.
Brief review of Kazemi et al.’s scheme
Kazemi et al.’s [28] scheme consists of the following six phases:
- System initialization: The trusted authority (TA) generates a cyclic group G1 with the order of a composite number N = p × q, where p and q are large prime numbers. By considering g1 is a member of G1 with the order of large prime number K, then TA selects a hash function
, a random numbers a as its master key, and computes
as its public key. Finally, TA publishes the system parameters {N, g1, G1, A1, h(⋅)}.
- Registration and key generation: Each user ui registers his/her personal information to TA. TA chooses a random number
to generates a dummy user identity
, for each ui. Next, TA computes the ui’s private key
, and provides the authentication key
to the ui. Finally, ui stores his/her
in the tamper-proof device (TPD), which is located in the on-board unit (OBU). The process of this phase is shown in Fig 3.
- Message generation: The ui encrypts
and
in
and
, respectively. Then, ui broadcasts the final message msg = 〈M ∥ W ∥ X ∥ T〉 to the other users in the network.
- Verify: The receiver verifies incoming message by checking whether
. If the condition hold, the message would be accepted, otherwise rejected. Both of message generation and verify process are shown in Fig 4.
- Tracking: In case of dispute, TA should be able to identifies
from its database that satisfies
. In this way, the TA can find that user’s identity and put it on the blacklist.
- Entering new TA: When a user ui enters the new TA region, it will send its
to the new TA. The new TA will firstly check if
, to proofs if ui is already been authenticated by the previous TA. If yes, with a similar process in the registration and key generation phase, the new TA will generate a new AK for ui to communicate in its area. Otherwise, the new TA does not issue a new
.
Cryptanalysis of Kazemi et al.’s scheme
Assume there is one user ui broadcasts two final messages msg = 〈M1 ∥ W1 ∥ X1 ∥ T1〉, 〈M2 ∥ W2 ∥ X2 ∥ T2〉 to another users in VANETs. Referred to the Euclid’s algorithm, if a and b are relatively prime then, there are exist integers h′ and k′ such that h′a + k′b = 1 [41]. By that property, if there is exit an adversary user in the network receiving those set of messages sent by the same user, the encrypting process of
and
in the message generation phase of Kazemi et al.’s scheme, could be impractical. The
can derive the secret parameter
from the set of messages in the following way:
(1)
(2)
(3)
(4)
In our case, if h(M1 ∥ T1) and h(M2 ∥ T2) are relatively prime, we can find r and s, such that rh(M1 ∥ T1) + sh(M2 ∥ T2) = 1. By doing operation (5), can acquire
based on (1) and (2).
(5)
Similarly,
can derive
with operation (6) based on (3) and (4):
(6)
From (5) and (6), any adversary can obtain the secret parameter
of the legal user ui by intercepting two messages msg = 〈M1 ∥ W1 ∥ X1 ∥ T1〉, 〈M2 ∥ W2 ∥ X2 ∥ T2〉. Furthermore, by this weakness, after deriving
from any user, the adversary
can impersonate them when entering a new TA’s region.
Improvement of Kazemi et al.’s scheme
After adversary gets (M1, T1) and (M2, T2) by intercepting messages msg = 〈M1 ∥ W1 ∥ X1 ∥ T1〉, 〈M2 ∥ W2 ∥ X2 ∥ T2〉 from user ui to RSU or other users uj,
calculates the hash functions h(M1 ∥ T1) and h(M2 ∥ T2). Next,
can find r and s through the Euclid’s algorithm, so that rh(M1 ∥ T1) + sh(M2 ∥ T2) = 1. Finally,
derives the secret parameter
of the legitimate user ui.
To overcome this problem, we have proposed two types of improvements. The first is to use the shared key sk between the sender and the receiver to encrypt the original message M in the message generation phase. The second alternative is to use Nonce instead of the timestamp T in the message generation, verify, and tracking phases.
First type improvement
In the first type improvement, the system initialization, registration and key generation, and entering new TA phases remain the same with Kazemi et al.’s [28]. Meanwhile, we modify the message generation phase, therefore the calculation in verify, and tracking phases also follow.
- Message generation: The ui encrypts
and
in
and
, respectively. Then, ui broadcasts the final message msg = 〈Encsk(M) ∥ W ∥ X ∥ T〉 to the other users in the network.
- Verify: The receiver verifies msg by checking whether
. If the condition hold, the message would be accepted, otherwise rejected.
- Tracking: In case of dispute, TA should be able to identifies
from its database that satisfies
. In this way, the TA can find that user’s identity and put it on the blacklist.
In this type of improvement, the adversary can intercepts broadcasted msg = 〈Encsk(M) ∥ W ∥ X ∥ T〉 but cannot perform (5) and (6). This is because the messages M1 and M2 are already encrypted. Thus
is unable to find r and s, so that rh(Encsk(M1)∥T1) + sh(Encsk(M2) ∥ T2) = 1 is not holds.
Second type improvement
In our second type improvement, the system initialization, registration and key generation, and entering new TA phases also remain the same with Kazemi et al.’s [28]. Meanwhile, we modify the message generation phase, therefore the calculation in verify, and tracking phases also follow. Remember, different from the timestamp, Nonce is only shared between the sender and the receiver. Once the verify phase is passed, the Nonce stored in the sender and receiver will be added by 1. The second type of improvement is given as follows:
- Message generation: The ui encrypts
and
in
and
, respectively. Then, ui broadcasts the final message msg = 〈M ∥ W ∥ X ∥ Nonce〉 to the other users in the network.
- Verify: The receiver verifies msg by checking whether
. If the condition hold, the message would be accepted, otherwise rejected.
- Tracking: In case of dispute, TA should be able to identifies
from its database that satisfies
. In this way, the TA can find that user’s identity and put it on the blacklist.
Similarly, in the second type improvement, can intercept the broadcasted msg = 〈M ∥ W ∥ X ∥ Nonce〉, but cannot perform (5) and (6) to derive
and
. This is because the messages’ Nonce1 and Nonce2 are only shared by the secrets of the sender and receiver.
cannot find r and s, so that rh(M1 ∥ Nonce1) + sh(M2 ∥ Nonce2) = 1 is not holds.
Analysis
This section analyzes the security and performance of our improved scheme.
Security analysis
Our proposed improvement is relatively the same as Kazemi et al.’s [28] scheme, except for shared secret key sk and Nonce utilization in the message generation phase. Therefore, some of the security requirements analyses remain the same. We compare the security analysis between Kazemi et al.’s [28], Horng et al.’s [34], Tzeng et al.’s [36], and our improvement scheme in Table 2.
Message authentication.
In the verify phase of Kazemi et al.’s scheme, upon receiving the final message msg = 〈Encsk(M) ∥ W ∥ X ∥ Nonce〉, receiver verifies it by checks whether . Since the message M is encrypted with the shared secret key sk between the verifier/receiver and the sender, so its only the addressed receiver who can decrypt the M. In addition, by our second improvement, only intended receiver who able to get the msg since it is shared with Nonce. Therefore,
cannot earn
and forge the ui.
Identity privacy-preserving.
The identity privacy-preserving is strongly related to how the adversary can derives the authentication key
of the user ui. As the final messages in our improvement are msg = 〈Encsk(M)∥W ∥ X ∥ T〉 and msg = 〈M ∥ W ∥ X ∥ Nonce〉, an adversary
cannot perform an infiltration to steal
and
from
and
, respectively, since finding modular eth roots is a hard problem [28]. By this condition, the vehicle user ui in this scheme can preserve their identity privacy.
Traceability.
The TA has a capability to derive of any user ui from
. Meanwhile, the mapping from dummy identities
to original identities
is done only in the TA. So, only TA can reveal the real identity of all entities in the network in the case of a dispute.
Unlinkability.
As elaborated in Section 3, by encrypting message M with the shared secret key sk between the sender and receiver Encsk(M), any adversaries cannot link the two messages sent by the same user ui. When
and
are calculated using Encsk(M), both
and
cannot be found by
using Euclid’s algorithm. Hence, both operation (5) and (6) are not hold. By this condition, our improved scheme reaches the unlinkability, and
is safe.
Performance analysis
This section discusses the computational complexity of the certificate and the signature verification process of our improved scheme. Let Th is the time required for performing a one-way hash function, and Tep1 is the time needed to perform exponentiation in G1. In the verify phase of Kazemi et al. [28], to authenticate the user ui and its message, the receiver is checking whether W × X = (A1)h(M‖T) mod N. From Table 3, we can see the complexity of performing one message verification is Th + Tep1 since it only requires one exponentiation in G1 of and one hash operation of h(M ∥ T). Meanwhile, to verify n messages, it has to perform n(Th + Tep1), because the scheme does not have a batch verification method to verify many messages at once. In our improved scheme, the verification cost for one and n messages are the same as Kazemi et al.’s, since the message encryption process Encsk gives no cost effect on efficiency. Besides [28], we also compared the efficiency of our improved scheme with two popular schemes [34, 36], that build on ID-based signatures. Since both schemes [34, 36] employ a bilinear map, map-to-point, and point multiplication over elliptic curve operations, they have to deal with a costly pairing operation Tpar, map-to-point hash operations TH, and point multiplication Tmul. We adopt an experiment in [42], which observes computation overhead in Python charm cryptographic library, on Intel Core i7-4765T 2.00 GHz and 8 GB RAM machine. The following results are obtained: Tpar is 1.34 ms, Tmul is 5.13 μs, TH is 0.0065 ms, Tep1 is 2.03 ms, and Th is 0.0001 ms. The comparison of computational complexity between Kazemi et al.’s [28], Horng et al.’s [34], Tzeng et al.’s [36], and our improvement scheme is presented in Table 3. From Tables 2 and 3, the proposed scheme is significantly better than other schemes in terms of security and message verification complexity.
Conclusion
In this paper, we proposed an improvement towards Kazemi et al.’s authentication scheme. Our investigation exhibited that an adversary can derive the user’s secret parameter
through Euclid’s algorithm property. Thus, it does not provide traceability and unlinkability, which leads to impersonation attacks. Since introducing two types of improvements, whether by encrypting the message M with a shared secret key sk between sender and receiver or putting a Nonce in the final message msg, we have proposed improving this scheme and making it secure.
References
- 1.
Raya M, Jungels D, Papadimitratos P, Hubaux JP. Certificate revocation in vehicular ad hoc network. Tech. Rep. Techincal LCA Report-2006-006, 20069, 2006.
- 2. Wang FY, Zeng D, Yang L. Smart cars on smart roads. IEEE Perv Comp. 2006;5(4):68–69.
- 3. Sheng X, Cheng X, Yang L, Zhang R, Jiao B. Data dissemination in VANETs: A scheduling approach. IEEE Transactions on Intelligent Transportation System, 2014; 15(5): 2213–2223.
- 4. Inam M, Li Z, Ali A, Zahoor A. A novel protocol for vehicle cluster formation and vehicle head selection in vehicular ad-hoc networks. International Journal of Electronics and Information Engineering, 2019; 10(2): 103–119.
- 5. Jeyaprakash T, Mukesh R. A survey of mobility models of vehicular ad hoc networks and simulators. International Journal of Electronics and Information Engineering, 2015; 2(2): 94–101.
- 6. Lu Z, Qu G, Liu Z. A survey on recent advances in vehicular network security, trust, and privacy. IEEE Transactions on Intelligent Transportation System, 2019; 20(2): 760–776.
- 7. Azees M, Vijayakumar P, Deborah LJ. Comprehensive survey on security services in vehicular ad-hoc networks. IET Intelligent Transport System, 2016; 10(6): 379–388.
- 8. Zhu X, Jiang S, Wang L, Li H. Efficient privacy-preserving authentication for vehicular ad hoc network. IEEE Transactions on Vehicular Technology, 2014; 63(2): 907–919.
- 9. Wang Y, Zhong H, Xu Y, Cui J. ECPB: Efficient conditional privacy preserving authentication scheme supporting batch verification for VANETs. International Journal of Network Security, 2016; 18(2): 374–382.
- 10. Sampigethaya K, Li M, Huang L, Poovendran R. AMOEBA: robust location privacy scheme for VANET. IEEE Journal on Selected Areas in Communications, 2007; 25(8): 1569–1589.
- 11. Raya M, Hubaux JP. Securing vehicular ad hoc networks. Journal of Computer Security, 2007; 15(1): 39–68.
- 12. Xie PS, Fu C, Feng T, Yan Y, Li LL. Malicious attack detection algorithm of internet of vehicles based on CW-KNN. International Journal of Network Security, 2020; 22(6): 1004–1014.
- 13. Faisal SM, Zaidi T. Timestamp based detection of sybil attack in VANET. International Journal of Network Security, 2020; 22(3): 3994–410.
- 14. Xie PS, Ma GQ, Feng T, Yan Y, Han XM. Forgery node detection algorithm based on dynamic reputation value in the internet of vehicles. International Journal of Network Security, 2020; 22(4): 655–663.
- 15. Xie PS, Fu C, Wang X, Feng T, Yan Y. Malicious atack pevention model of internet of vehicles based on IOV-SIRS. International Journal of Network Security, 2020; 23(5): 835–844.
- 16.
Huang Q, Li N, Zhang Z, Yang Y. Secure and privacy-preserving warning message dissemination in cloud-assisted internet of vehicles. 2019 IEEE Conference on Communications and Network Security (CNS), 2019.
- 17. Aalsalem MY, Khan WZ, Gharibi W, Khan MK, Arshad Q. Wireless Sensor Networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges. Journal of Network and Computer Applications, 2018; 113: 87–97.
- 18. Zhou J, Dong X, Cao Z, Vasilakos AV. Secure and privacy preserving protocol for cloud-based vehicular DTNs. IEEE Transactions on Information Forensics and Security, 2015; 10(6): 1299–1314.
- 19. Waqar A, Raza A, Abbas H, Khan MK. A framework for preservation of cloud users’ data privacy using dynamic reconstruction of metadata. Journal of Network and Computer Applications, 2013; 36: 235–248.
- 20. Xie PS, Han XM, Feng T, Yan Y, Ma GQ. A method of constructing arc edge anonymous area based on LBS privacy protection in the internetof vehicles. International Journal of Network Security, 2020; 22(2): 275–282.
- 21. Xie PS, Han XM, Feng T, Yan Y, Ma GQ. An algorithm of the privacy security protection based on location service in the internet of vehicles. International Journal of Network Security, 2019; 21(4): 556–565.
- 22. Vijayakumar P, Azees M, Kannan A, Deborah LJ. Dual authentication and key management techniques for secure data transmission in vehicular ad hoc networks. IEEE Transactions on Intelligent Transportation Systems, 2016; 17(4): 1015–1028.
- 23. Khan MK. Fingerprint biometric–based self-authentication and deniable authentication schemes for the electronic world. IETE Technical Review, 2009; 26(3); 191–195.
- 24.
Lu R, Lin X, Zhu H, Ho PH, Shen X. ECPP: Eefficient conditional privacy preservation protocol for secure vehicular communications. Proc. 27th Conf. Comput. Commun. (INFOCOM), 2008 Apr; 1229–1237.
- 25.
Zhang C, Lin X, Lu R, Ho PH. RAISE: An efficient RSU-aided message authentication scheme in vehicular communication networks. Proc. IEEE Int. Conf. Commun., 2008 May; 1451–1457.
- 26. Rajput U, Abbas F, Oh H. A hierarchical privacy preserving pseudonymous authentication protocol for VANET. IEEE Access, 2016; 4: 7770–7784
- 27. Azees M, Vijayakumar P, Deborah LJ. EAAP: Efficient anonymous authentication with conditional privacy-preserving scheme for vehicular ad hoc networks. IEEE Transactions on Intelligent Transportation Systems, 2017; 18(9): 2467–2476.
- 28.
Kazemi M, Delavar M, Mohajeri J, Salmasizadeh M. On the security of an efficient anonymous authentication with conditional privacy-preserving scheme for vehicular ad hoc networks. 26th Iranian Conference on Electrical Engineering (ICEE 2018), 2018; 510–514.
- 29. Lin X, Sun X, Wang X, Zhang C, Ho PH, Shen X. TSVC: timed efficient and secure vehicular communications with privacy preserving. IEEE Transactions on Wireless Communications, 2008; 7(12); 4987–4998.
- 30. Chang CC, Yang JH, Wu YC. An efficient and practical authenticated communication scheme for vehicular ad hoc networks. International Journal of Network Security, 2015; 17(6); 702–707.
- 31.
Lee CC, Lai YM, Cheng PJ. An efficient multiple session key establishment scheme for VANET group integration. 2015 IEEE Intelligent Vehicles Symposium (IV), 2015; 1316–1321.
- 32.
Zhang C, Lu R, Lin X, Ho PH, Shen X. An efficient identity-based batch verification scheme for vehicular sensor networks. IEEE INFOCOM 2008—The 27th Conference on Computer Communications, 2008; 816–824.
- 33. Lee CC, Lai YM. Toward a secure batch verification with group testing for VANET. Wireless Networks, 2013; 19; 1441–1449.
- 34. Horng SJ, Tzeng SF, Pan Y, Fan P, Wang X, Li T, et al. b-SPECS+: Batch verification for secure pseudonymous authentication in VANET. IEEE Transactions on Information Forensics and Security, 2013; 8(11); 1860–1875.
- 35. Jianhong Z, Min X, Liying L. On the security of a secure batch veriflcation with group testing for VANET. International Journal of Network Security, 2014; 16(4); 313–320.
- 36. Tzeng SF, Horng SJ, Li T, Wang X, Huang PH, Khan MK. Enhancing security and privacy for identity-based batch verification scheme in VANETs. IEEE Transactions on Vehicular Technology, 2017; 66(4); 3235–3248.
- 37. Wang L, Zheng D, Guo R, Hu CC, Jing CM. A blockchain-based privacy-preserving authentication scheme with aonymous identity in vehicular networks. International Journal of Network Security, 2020; 22(6); 981–990.
- 38. Mei Q, Xiong H, Chen J, Yang M, Kumari S, Khan MK. Efficient certificateless aggregate signature with conditional privacy preservation in IoV. IEEE Systems Journal, 2021; 15(1), 245–256.
- 39. Thumbur G, Rao GS, Reddy PV, Gayathri NB, Reddy DVRK, Padmavathamma M. Efficient and secure certificateless aggregate signature-based authentication scheme for vehicular ad hoc networks. IEEE Internet of Things Journal, 2021; 8(3); 1908–1920.
- 40. Zeng S, Huang Y, Liu X. Privacy-preserving communication for VANETs with conditionally anonymous ring signature. International Journal of Network Security, 2015; 17(2); 135–141.
- 41.
Keedwell AD. 92.31 Euclid’s algorithm and the money changing problem. 2008;524.
- 42. Nabil M, Bima M, Alsharif A, Johnson W, Gunukula S, Mahmoud M, et al. Priority-based and privacy-preserving electric vehicle dynamic charging system with divisible e-payment. Smart Cities Cyber. Priv., 2019: 165–186.