Collaborative Data Anonymization for Privacy-Preserving Vehicular Ad-hoc Network

Optimization of data usability and privacy protection is a challenging goal. Although the released data are sanitized with the upgraded model of data semantic changer, always face privacy and scalability issues. On the other hand, local anonymization may not be suitable in a multi-nodal environment such as a vehicular network. Alternatively, hiding sensitive attributes are essential alongside discreet data privacy. In this paper, we proposed a collaborative privacy-preserving data anonymization technique for the vehicular network. Moreover, the method is capable of achieving a desirable level of data sanitization in a group. The use of k-anonymity, l-diversity and t-closeness make the proposed technique more strong to protect data privacy in different stages of communication. Furthermore, the performed analysis of the proposed scheme ensures usability and practicality. Lastly, the future direction of the stipulated field shows the research path in collaborative data anonymization in the vehicular environment.