Privacy by Design Approach for Vehicular Tripdata Using k-Anonymity Perturbation

Vehicular communication in intelligent transport system offers data dissemination among vehicles in rapid transmission of road incident log to trusted entities. The adversary attacks having background knowledge are often a side effect due to re-identity and linkage attacks by innocuous public data sharing provisions. The proposed work spotlight on attacks with background knowledge who attempts to extract individual’s data using high end data extraction algorithms by linking with the vehicular trip database. Enhancing location privacy and individual privacy is achievable with k-anonymity perturbation scheme applied on vehicular database, which shows trivial for data leakage attacks, and the proposed algorithm significantly reduces the vehicle uniqueness to zero in achieving the sanitization process of vehicular database to avoid pre-knowledge attacks by intruder of ITS. This approach shows resilience in implementing the privacy preservation with lightweight process implementation and non-polynomial time complexity for DENIM messages. The data distortion caused due to perturbation is analyzed and reported in this work.