Intelligent transport system (ITS), owing to its potential to enhance road safety, comfort, security, and traffic efficiency have attracted attention from automotive industries and academia in recent years. The underlying technology i.e., vehicular ad-hoc networks (VANETs) provide a means for vehicles to intelligently exchange messages regarding road and traffic conditions to enhance safety. Wireless communication in ITS leads to many security and privacy challenges. Security and privacy of ITS are important issues that demand incorporation of confidentiality, privacy, authentication, integrity, non-repudiation, and restrictive obscurity. In order to ensure the privacy of vehicles during communication, it is required that the real identity of vehicles should not be revealed. There must be robust and efficient security and privacy mechanisms for the establishment of a reliable and trustworthy network. In this thesis, an efficient pseudonym based secure framework is proposed for preserving the real identities of vehicles. The proposed distributed architecture allows vehicles to generate pseudonyms in a very private and secure way. In the absence of a distributed architecture, the privacy cannot be preserved by storing information regarding vehicles in a single location. The proposed framework only allows vehicles with valid pseudonyms to communicate in ITS. Pseudonyms are assigned to vehicles in a secure manner. The pseudonym mappings of vehicles are stored at different locations to avoid any chance of vehicle pseudonyms certificates linkability. In addition, the most recent communication pseudonyms of a malicious vehicle are revoked and are stored in the Certificate Revocation List (CRL) that results in small size of the CRL. Therefore, the CRL size does not increase exponentially. The distributed proposed framework guarantees, the vehicles privacy preservation in the real identities mapping and revocation phase from the certificate authorities. The empirical results prove that the proposed framework is robust and efficient with low computational cost, overhead ratio, average latency, and an increased delivery ratio.