Skip to main content
SpringerLink
Log in

A Federated Learning Assisted Conditional Privacy Preserving Scheme for Vehicle Networks

  • Conference paper
  • First Online:
Cyberspace Safety and Security (CSS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 13172))

Included in the following conference series:

Abstract

As the development of mobile communication technologies, Vehicle Networks can not only improve the efficiency of traffic operation, but also enhance the intelligent management level of traffic services. However, Vehicle Networks also bring a series of challenges, such as information leakage and message manipulation. In this paper, we introduce a novel federated learning assisted privacy preserving scheme for Vehicle Networks. In the proposed scheme, pseudonym is employed to hide the real identity of the vehicle, and homomorphic encryption is used to protect the private information in the training and aggregation processes. Moreover, the system is assisted with federated learning and fog computing. This not only improves efficiency in data integration and transmission, but also contributes to a more flexible and controllable traffic system. Security analyses demonstrate that the scheme meets the desirable security requirements, such as correctness, conditional privacy preserving and message authentication. And compared with some existing schemes, our proposed scheme enjoys better efficiency in both computation and communication.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bayat, M., Barmshoory, M., Rahimi, M.: A secure authentication scheme for VANETs with batch verification. Wireless Netw. 21(5), 1733–1743 (2015)

    Article  Google Scholar 

  2. Bo, X.-L.: Analysis of the security mechanism of internet of vehicles communication based on public key cryptography. Internet Things Technol. 5(8), 32–34 (2018)

    Google Scholar 

  3. Bresson, E., Catalano, D., Pointcheval, D.: A simple public-key cryptosystem with a double trapdoor decryption mechanism and its applications. In: Laih, C.-S. (ed.) ASIACRYPT 2003. LNCS, vol. 2894, pp. 37–54. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-40061-5_3

    Chapter  Google Scholar 

  4. Cao, B., Sun, Z.-H., Zhang, J.-T.: Resource allocation in 5G IoV architecture based on SDN and fog-cloud computing. IEEE Trans. Intell. Transp. Syst. 22, 1–9 (2021)

    Article  Google Scholar 

  5. Hao, F.: A 2-round anonymous veto protocol. In: Christianson, B., Crispo, B., Malcolm, J.A., Roe, M. (eds.) Security Protocols 2006. LNCS, vol. 5087, pp. 212–214. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04904-0_29

    Chapter  Google Scholar 

  6. Gentry, C.: Certificate-based encryption and the certificate revocation problem. In: Biham, E. (ed.) EUROCRYPT 2003. LNCS, vol. 2656, pp. 272–293. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-39200-9_17

    Chapter  Google Scholar 

  7. Gu, C.-X., Zhu, Y.-F., Pan, X.-Y.: Forking lemma and the security proofs for a class of ID-based signatures. J. Softw. 18(4), 1007–1014 (2007)

    Article  MathSciNet  Google Scholar 

  8. He, D.-B., Zeadally, S., Xu, B.-W.: An efficient identity-based conditional privacy-preserving authentication scheme for vehicular ad hoc networks. IEEE Trans. Inf. Forensics Secur. 10(12), 2681–2691 (2015)

    Article  Google Scholar 

  9. Horng, S.-J., Tzeng, S.-F., Huang, P.-H., Wang, X.-M.: An efficient certificateless aggregate signature with conditional privacy-preserving for vehicular sensor networks. Inf. Sci. Int. J. 317(3), 48–66 (2015)

    MathSciNet  MATH  Google Scholar 

  10. Hwang, J.-Y., Song, B., Choi, D.: Simplified small exponent test for batch verification. Theoret. Comput. Sci. 662, 48–58 (2016)

    Article  MathSciNet  Google Scholar 

  11. Kamat, P., Baliga, A., Wade, T.: An identity-based security framework for VANETs. In: International Workshop on Vehicular Ad Hoc Networks. ACM (2006)

    Google Scholar 

  12. Li, J.-L., Choo, K.-K.-R., Zhang, W.-G.: EPA-CPPA: an efficient, provably-secure and anonymous conditional privacy-preserving authentication scheme for vehicular ad hoc networks. Veh. Commun. 13(1), 104–113 (2018)

    Google Scholar 

  13. Liu, H., Li, H.: A distributed authentication protocol for VANET. J. Xi’an Jiaotong Univ. 47(2), 58–62 (2013)

    Google Scholar 

  14. Lu, R..-X.., Lin, X.-D., Luan, T.-H.: Pseudonym changing at social spots: an effective strategy for location privacy in VANETs. IEEE Trans. Veh. Technol. 61(1), 86–96 (2012)

    Article  Google Scholar 

  15. Lu, R.-X., Lin, X.-D., Zhu, H.-J.: ECPP: efficient conditional privacy preservation protocol for secure vehicular communications. In: INFOCOM the Conference on Computer Communications IEEE. IEEE (2008)

    Google Scholar 

  16. Ng, J.-S., Lim, W.-Y.-B., Dai, H.-N.: Communication-efficient federated learning in UAV-enabled IOV: a joint auction-coalition approach. In: GLOBECOM 2020–2020 IEEE Global Communications Conference. IEEE (2020)

    Google Scholar 

  17. Palanisamy, B., Liu, L.: MobiMix: protecting location privacy with mix-zones over road networks. In: Proceedings of the 27th International Conference on Data Engineering, ICDE 2011, Hannover, Germany, 11–16 April 2011. IEEE (2011)

    Google Scholar 

  18. Raya, M., Jean-Pierre, H.: Securing vehicular ad hoc networks. J. Comput. Secur. 15(1), 39–68 (2007)

    Article  Google Scholar 

  19. Shao, J., Lin, X.-D., Lu, R.-X.: A threshold anonymous authentication protocol for VANETs. IEEE Trans. Veh. Technol. 65(3), 1711–1720 (2015)

    Article  Google Scholar 

  20. Shim, K.-A.: CAPS: an efficient conditional privacy-preserving authentication scheme for vehicular sensor networks. IEEE Trans. Veh. Technol. 61(4), 1874–1883 (2012)

    Article  Google Scholar 

  21. Wang, M., Wu, J., Li, G.-L.: Toward mobility support for information-centric IoV in smart city using fog computing. In: 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE). IEEE (2017)

    Google Scholar 

  22. Xu, G.-W., Li, H.-W., Liu, S.: VerifyNet: secure and verifiable federated learning. IEEE Trans. Inf. Forensics Secur. 15, 911–926 (2019)

    Article  Google Scholar 

  23. Rong, Yu., Kang, J.-W., Huang, X.-M.: MixGroup: accumulative pseudonym exchanging for location privacy enhancement in vehicular social networks. IEEE Trans. Dependable Secure Comput. 13(1), 93–105 (2016)

    Article  Google Scholar 

  24. Zhang, J., Cui, J., Zhong, H.: PA-CRT: Chinese remainder theorem based conditional privacy-preserving authentication scheme in vehicular ad-hoc networks. IEEE Trans. Dependable Secure Comput. 18(2), 722–735 (2019)

    Article  Google Scholar 

  25. Zhang, Z.-H., Li, Q.-D., Fu, Y.: Adaptive federated deep learning with non-IID data. Acta Automatica Sinica, 1–13 (2021)

    Google Scholar 

  26. Zhou, C.-Y., Fu, A.-M., Shui, Yu.: Privacy-preserving federated learning in fog computing. IEEE Internet Things J. 7(11), 10782–10793 (2020)

    Article  Google Scholar 

  27. Zhu, C.-S., Liu, P.-H., Wang, Q.-R.: Group key management scheme based on group signature with fault tolerance for mobile ad hoc networks. Appl. Res. Comput. 28(10), 3811–3816 (2011)

    Google Scholar 

Download references

Acknowledgement

This work was partially supported by the National Natural Science Foundation of China (Grant No. 62072134, 61702168, U2001205), Key Research and Development Program of Hubei Province (Grant No. 2020AAA001), and Guangxi Key Laboratory of Trusted Software (Grant No. KX201908). We are also grateful to the anonymous reviewers for their valuable comments on the paper.

Author information

Authors and Affiliations

  1. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China

    Zhe Xia & Yifeng Shu

  2. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Zhe Xia

  3. School of Computers, Hubei University of Technology, Wuhan, 430068, China

    Hua Shen & Mingwu Zhang

Authors
  1. Zhe Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yifeng Shu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hua Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mingwu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhe Xia .

Editor information

Editors and Affiliations

  1. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

  2. University of Padua, Padua, Italy

    Mauro Conti

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xia, Z., Shu, Y., Shen, H., Zhang, M. (2022). A Federated Learning Assisted Conditional Privacy Preserving Scheme for Vehicle Networks. In: Meng, W., Conti, M. (eds) Cyberspace Safety and Security. CSS 2021. Lecture Notes in Computer Science(), vol 13172. Springer, Cham. https://doi.org/10.1007/978-3-030-94029-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-94029-4_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94028-7

  • Online ISBN: 978-3-030-94029-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation