Skip to main content
SpringerLink
Log in

An innovative privacy preservation and security framework with fog nodes in enabled vanet system using hybrid encryption techniques

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Effective communication between smart transportation and smart vehicles is carried out using Vehicular Ad-Hoc Networks (VANETs). Here, the VANET systems that exist nowadays have issues regarding user privacy and authentication. In internal vehicles, the fake message broadcasting should be stopped to protect the vulnerability of these vehicles from privacy issues. Additionally, the traditional manner of storing transmitted data lacks a decentralized and distributed security system, making it easily vulnerable for third parties to provoke malicious activities within the VANET system. VANET is an autonomous and open-access network, so, privacy and security are the main issues. Hence, it is essential to rectify the complications that are present in the traditional security and privacy preservation models in the VANET system. Thus, an innovative privacy preservation and security scheme with fog enabled VANET system is implemented by considering the complications in the existing models. The major that take place in the recommended framework are (a) Node Authentication, (b) Privacy Preservation, and (c) Message Verification. Initially, the node authentication is performed in the recommended framework using an Adaptive Deep Bayesian network (ADBN) in order to ensure an enhanced permissibility rate in the vehicular node. Then, messages are authenticated to protect the virtue of the messages. The parameters in the ADBN are tuned using the aid of Integrated Fire Hawk with Tunicate Swarm Algorithm (IFHTSA). Next, the privacy preservation procedure in the VANET model is carried out using Hybrid Attribute-Based Advanced Encryption Standard (HABAES) encryption techniques. The keys obtained on the encryption of the messages are signed digitally. Moreover, the suggested model utilized the fog node for the analysis instead of Road-Side Units (RSUs), because of its effectiveness in minimizing the latency rate with an increased throughput rate. In the message verification node, once the Fog Edge Node (FEN) receives the signed message from the vehicles, then it checks the validity of the vehicle node by comparing it with the signed messages. Finally, the experimentation is done based on various standard performance metrics. However, the developed model achieves 94% and 93% in terms of accuracy and precision. Hence, the suggested technique offers minimal computation and communication overhead in different experimental observations over the classical technique.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Algorithm 1
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The required data for the Privacy Preservation and Security in VANET is collected manually.

Abbreviations

\(L{F}_{D}^{SK}\) :

Collected Data

\(om\left(t\right)\) :

Output and Input

\(M\) :

Network Weight

\(G\left(v=b|y,M\right)\) :

Label’s probability

\(X\) :

Time

\({\widehat{M}}_{x}\) :

Dropout function

\(I\) :

Training data

\(X\) :

Precision

\(X{I}_{H}^{adb}\) :

Optimized steps per epoch in ADBN

\(LN\) :

Objective Function

\(N{L}_{R}^{adb}\) :

Hidden neuron count

\(O\) :

Accuracy

\(T{Y}_{I}^{adb}\) :

Epoch in ADBN

\(Fpv\) and \(Tpv\) :

False and true positive values

\(Tgv\) and \(Fgv\) :

True and false negative values

\(VD\) :

Standard deviation

\({Y}_{t}^{NP}\) :

FHO position

\(DN\) :

Median value

\(\overrightarrow{FS}\) :

TSO position

\(I\) :

New position

\(a\) :

Random number

\({f}_{r,MX}^{s}\) and \({f}_{r,MN}^{s}\) :

Maximum and minimum bounds

\(T\) :

Total count of candidate solution

\(D\) :

Candidate solution dimension

\(B\) :

Total count of fire hawks

\(j\) :

Total count of prey

\(\left({a}_{1,}{b}_{1}\right)\) and \(\left({a}_{2,}{b}_{2}\right)\) :

Coordinate of prey and fire hawks

\({L}_{h}^{i}\) :

Distance among prey and fire hawk

\({a}_{1}\) and \({a}_{2}\) :

Random variables

\({B}_{Cl}\) :

Fire hawks neighbor

\({B}_{i}^{NP}\) :

New position of fire hawk

\(H{S}_{i}\) :

Hiding place for prey

\({Y}_{t}^{NP}\) :

Prey’s new position

\({B}_{OB}\) :

Fire hawks

\({d}_{1},{d}_{2}\) and \({d}_{3}\) :

Random values

\(\overrightarrow{W}\) :

Water flow direction

\(\overrightarrow{A}\) :

Gravitational force

\(\overrightarrow{V}\) :

Search agent position

\({e}_{MX}\) and \({e}_{MN}\) :

Initial subordinate and subordinate speed

\({d}_{r}\) :

Random value

\(\overrightarrow{pf}\) :

Food position

\(\overrightarrow{FS}\) :

Distance among search agent and food

\(K{E}^{be}\) :

Master key

\(K\) :

Decryption key

\(A{H}_{S}^{MU}\) :

Encrypted data

\({R}_{n}\) :

Total number of rounds

\(RE\) :

Round key

\(XL\) :

Mixcolumns

\(HW\) :

Shiftrows

\(BY\) :

Subbytes

\(O{F}_{N}^{RE}\) :

Final encrypted key

References 

  1. Sun Y, Lu R, Lin X, Shen X, Su J (2010) An Efficient Pseudonymous Authentication Scheme With Strong Privacy Preservation for Vehicular Communications. IEEE Trans Veh Technol 59(7):3589–3603

    Article  Google Scholar 

  2. Li J, Lu H, Guizani M (2015) ACPN: A Novel Authentication Framework with Conditional Privacy-Preservation and Non-Repudiation for VANETs. IEEE Trans Parallel Distrib Syst 26(4):938–948

    Article  Google Scholar 

  3. Cheng H, Shojafar M, Alazab M, Tafazolli R, Liu Y (2022) PPVF: Privacy-Preserving Protocol for Vehicle Feedback in Cloud-Assisted VANET. IEEE Trans Intell Transp Syst 23(7):9391–9403

    Article  Google Scholar 

  4. Goudarzi S, Soleymani SA, Anisi MH, Azgomi MA, Movahedi Z, Kama N, Rusli HM, Khan MK (2022) A privacy-preserving authentication scheme based on Elliptic Curve Cryptography and using Quotient Filter in fog-enabled VANET. Ad Hoc Networks 128:102782

    Article  Google Scholar 

  5. Liang Y, Liu Y (2023) Analysis and Improvement of an Efficient Certificateless Aggregate Signature with Conditional Privacy Preservation in VANETs. IEEE Syst J 17(1):664–672

    Article  MathSciNet  Google Scholar 

  6. Tzeng SF, Horng SJ, Li T, Wang X, Huang PH, Khan MK (2017) Enhancing Security and Privacy for Identity-Based Batch Verification Scheme in VANETs. IEEE Trans Veh Technol 66(4):3235–3248

    Article  Google Scholar 

  7. Lu R, Lin X, Liang X, Shen X (2012) A Dynamic Privacy-Preserving Key Management Scheme for Location-Based Services in VANETs. IEEE Trans Intell Transp Syst 13(1):127–139

    Article  Google Scholar 

  8. Rajput U, Abbas F, Oh H (2016) A Hierarchical Privacy Preserving Pseudonymous Authentication Protocol for VANET. IEEE Access 4:7770–7784

    Article  Google Scholar 

  9. Cui J, Wen J, Han S, Zhong H (2018) Efficient Privacy-Preserving Scheme for Real-Time Location Data in Vehicular Ad-Hoc Network. IEEE Internet Things J 5(5):3491–3498

    Article  Google Scholar 

  10. Wang F, Xu Y, Zhang H, Zhang Y, Zhu L (2016) 2FLIP: A Two-Factor Lightweight Privacy-Preserving Authentication Scheme for VANET. IEEE Trans Veh Technol 65(2):896–911

    Article  Google Scholar 

  11. Xia Y, Chen W, Liu X, Zhang L, Li X, Xiang Y (2017) Adaptive Multimedia Data Forwarding for Privacy Preservation in Vehicular Ad-Hoc Networks. IEEE Trans Intell Transp Syst 18(10):2629–2641

    Article  Google Scholar 

  12. Tangade S, Manvi SS, Lorenz P (2018) Decentralized and Scalable Privacy-Preserving Authentication Scheme in VANETs. IEEE Trans Veh Technol 67(9):8647–8655

    Article  Google Scholar 

  13. Nandy T, Idris MYI, Md Noor R, Wahab AWA, Bhattacharyy S (2021) A Secure, Privacy-Preserving, and Lightweight Authentication Scheme for VANETs. IEEE Sensors Journal 21(18):20998–21011

    Article  Google Scholar 

  14. Shen J, Liu D, Chen X, Li J, Kumar N, Vijayakumar P (2020) Secure Real-Time Traffic Data Aggregation With Batch Verification for Vehicular Cloud in VANETs. IEEE Trans Veh Technol 69(1):807–817

    Article  Google Scholar 

  15. Peixoto MLM, Maia AHO, Mota E, Rangel E, Costa DG, Turgut D, Villas LA (2021) "A traffic data clustering framework based on fog computing for VANETs. Vehicular Communications 31:100370

    Article  Google Scholar 

  16. Al-Ani R, Baker T, Zhou B, Shi Q (2023) Privacy and safety improvement of VANET data via a safety-related privacy scheme. Int J Inf Secur 22(4):1–21

    Article  Google Scholar 

  17. Hussain R, Rezaeifar Z, Lee Y-H, Oh H (2015) "Secure and privacy-aware traffic information as a service in VANET-based clouds. Pervasive and Mobile Computing 24:194–209

    Article  Google Scholar 

  18. Soleymani SA, Goudarzi S, Mohammad HA, Mahdi Z, Abdul HA, Kama N (2021) A security and privacy scheme based on node and message authentication and trust in fog-enabled VANET. Vehicular Communications 29:100335

    Article  Google Scholar 

  19. Zheng D, Jing C, Guo R, Gao S, Wang L (2019) A Traceable Blockchain-Based Access Authentication System With Privacy Preservation in VANETs. IEEE Access 7:117716–117726

    Article  Google Scholar 

  20. Biswas S, Misic J (2015) "A Cross-Layer Approach to Privacy-Preserving Authentication in Wave-Enabled VANETs. IEEE Trans Vehicular Technol 65:5

    Google Scholar 

  21. Sun C, Liu J, Xu X, Ma J (2017) A Privacy-Preserving Mutual Authentication Resisting DoS Attacks in VANETs. IEEE Access 5:24012–24022

    Article  Google Scholar 

  22. Alharthi A, Ni Q, Jiang R (2021) A Privacy-Preservation Framework Based on Biometrics Blockchain (BBC) to Prevent Attacks in VANET. IEEE Access 9:87299–87309

    Article  Google Scholar 

  23. Feng X, Shi Q, Xie Q, Liu L (2021) An Efficient Privacy-preserving Authentication Model based on blockchain for VANETs. J Systems Architecture 117:102158

    Article  Google Scholar 

  24. Wang M, Liu D, Zhu L, Yongjun X, Wang F (2016) "LESPP: lightWeight and efficient strong privacy preserving authentication scheme for secure VANET communication. Computing 98:685–708

    Article  MathSciNet  Google Scholar 

  25. Moni SS, Manivannan D (2021) "A scalable and distributed architecture for secure and privacy-preserving authentication and message dissemination in VANETs. Internet of Things 13:100330

    Article  Google Scholar 

  26. Tanveer M, Alkhayyat A, Naushad A, Khan AU, Kumar N, Alharbi AG (2022) RUAM-IoD: A Robust User Authentication Mechanism for the Internet of Drones. IEEE Access 10:19836–19851

    Article  Google Scholar 

  27. Tanveer M, Alasmary H, Kumar N, Nayak A (2023) SAAF-IoD: Secure and Anonymous Authentication Framework for the Internet of Drones. IEEE Trans Veh Technol (Early Access) 1–13

  28. Tanveer M, Khan AU, Kumar N, Hassan MM (2022) RAMP-IoD: A Robust Authenticated Key Management Protocol for the Internet of Drones. IEEE Internet Things J 99:1–1

    Google Scholar 

  29. Arif M, Wang G, Balas VE, Geman O, Castiglione A, Chen J (2020) SDN based communications privacy-preserving architecture for VANETs using fog computing. Vehicular Communications 26:100265

    Article  Google Scholar 

  30. Bodenstedt S, Rivoir D, Jenke A, Wagner M, Breucha M, Müller-Stich B, Mees ST, Weitz J, Speidel S (2019) Active learning using deep Bayesian networks for surgical workflow analysis. Computer Vision and Pattern Recognition 14:1079–1087

    Google Scholar 

  31. Azizi M, Talatahari S, Gandomi AH (2022) Fire hawk optimizer: a novel metaheuristic algorithm. Artif Intell Rev 56(1):1–77

    Google Scholar 

  32. Kaur S, Awasthi LK, Sangal AL, Dhiman G (2020) Tunicate swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artificial Intel 90

  33. Jason Hinek M, Jiang S, Safavi-Naini R, Shahandashti Siamak F (2012) Attribute-based encryption without key cloning. Int J Appl Cryptography 2(3)

  34. Tezcan C (2021) Optimization of advanced encryption standard on graphics processing units 9: 67315–67326

  35. De Vita F, Bruneo D (2019) On the use of LSTM networks for Predictive Maintenance in Smart Industries 241–248

  36. Sherstinsky A (2020) Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Physica D 404:132306

    Article  MathSciNet  Google Scholar 

  37. Kumar V, Arora H, Sisodia J (2020) ResNet-based approach for detection and classification of plant leaf diseases 495–502

  38. Umucu EH (2022) Elliptic curve cryptography in blockchain technology, FINRA

  39. Zhong C, Li G, Meng Z (2022) Beluga whale optimization: a novel nature-inspired metaheuristic algorithm. Knowl-Based Syst 251:109215

    Article  Google Scholar 

  40. Giernacki W, Espinoza Fraire T, Kozierski P (2017) Cuttlefish optimization algorithm in autotuning of altitude controller of Unmanned Aerial Vehicle (UAV)

Download references

Funding

This research did not receive any specific funding.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India

    M. S. Minu

  2. Department of Agricultural Economics, Anbil Dharmalingam Agicultural College and Research Institute, Thiruchy, Tamil Nadu, 625104, India

    P. Jona Innisai Rani

  3. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522302, India

    Vijaya Krishna Sonthi

  4. Department of Computer Science and Engineering, R.M.D Engineering College, RMD College Rd, Kavaraipettai, Tamil Nadu, 601206, India

    G. Shankar

  5. Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Kuthambakkam, Chennai-600124, Tamil Nadu, India

    E Mohan & A. Rajesh

Authors
  1. M. S. Minu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Jona Innisai Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vijaya Krishna Sonthi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Shankar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E Mohan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. A. Rajesh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to E Mohan.

Ethics declarations

Ethical approval

Not Applicable.

Consent to publish

Not Applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (HTML 34 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Minu, M.S., Rani, P.J.I., Sonthi, V.K. et al. An innovative privacy preservation and security framework with fog nodes in enabled vanet system using hybrid encryption techniques. Peer-to-Peer Netw. Appl. (2024). https://doi.org/10.1007/s12083-024-01672-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12083-024-01672-4

Keywords

Search

Navigation