**10. Conclusions**

This paper takes on the problem of location privacy in a vehicular network. We have proposed a new Crowd-based Mix Context (CMC) scheme for location privacy preservation in the vehicular network. CMC employs vehicle speed, direction, and traffic density for the pseudonym changing process. Based on these parameters, the vehicles update pseudonyms simultaneously, which creates confusion for an adversary to break the pseudonyms of vehicles at different location spots. We formally model and analyze the proposed scheme using HLPN. The evaluation results show that CMC improves the anonymisation of vehicles compared with existing schemes IndMZ and TAPCS at various traffic densities. This prevents the adversary from linking pseudonyms of vehicles and identifies a target vehicle in the road region. The proposed scheme reduced the computation burden on vehicles for generating fake pseudonyms in the existing methods. The CMC also minimizes the impact of anonymisation on safety applications by managing road context information. In the future, we are eager to do more experiments on the vehicle high speed and low traffic density and will determine a robust privacy preservation method in such a dynamic road network condition.

**Author Contributions:** Conceptualization, I.U. and M.A.S.; methodology, A.K.; software, I.U.; validation, A.W., M.A.S., G.J. and A.K.; formal analysis, I.U., G.J. and A.W.; investigation, C.M.; resources, C.M.; data curation, A.K.; writing—original draft preparation, I.U.; writing—review and editing, M.A.S., G.J. and A.W.; visualization, G.J., I.U. and A.W.; supervision, M.A.S.; project administration, A.K. and C.M.; funding acquisition, C.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** Ikram Ullah wants to thank the Higher Education Commission Pakistan for supporting PhD studies. Maple would like to acknowledge the support of UKRI through the grants EP/R007195/1 (Academic Centre of Excellence in Cyber Security Research—University of Warwick), EP/N510129/1 (The Alan Turing Institute), EP/R029563/1 (Autotrust), and EP/S035362/1 (PETRAS, the National Centre of Excellence for IoT Systems Cybersecurity).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Thanks to Higher Education Commission, Pakistan for supporting academic studies.

**Conflicts of Interest:** The authors declare no conflict of interest.
