Vehicular Ad-Hoc Networks (VANETs) have significantly enhanced driving safety and comfort by leveraging vehicular wireless communication technology. Due to the open nature of VANETs, conditional privacy-preserving authentication protocol should be offered against potential attacks. Efficient and secure authentication among vehicles in VANETs are important requirements, but there are various limitations in the existing conditional privacy-preserving authentication protocols for securing VANETs. To cope with the inherent issues, we propose a conditional privacy-preserving authentication protocol based share group session key (SGSK) by integrating the self-healing key distribution technique, blockchain, and MTI/C0 protocol. In our protocol, we use SGSK instead of the time-consuming Certificate Revocation List (CRL) checking, and we revoke malicious vehicles by updating SGSK. It is shared among unrevoked vehicles within a domain and across-domain. As a result, when a malicious revoked vehicle enters a new domain, it is difficult for it to access the system and send false messages. Furthermore, our protocol can not only achieve computation efficiency by reducing the number of computing operations of bilinear pairing but also resist various attacks while keeping conditional privacy protection. We implement our protocol in the Hyperledger Fabric platform. The experimental results show that our protocol is available to revoke 180 malicious vehicles in across-domain scenarios within one second, and it can meet the requirement of verifying 600 messages per second easily. Moreover, our comprehensive performance evaluations demonstrates that our protocol outperforms other approaches in terms of vehicle revocation checking cost, computation overhead, and communication overhead. In addition, to show the feasibility and validity of our protocol, we use SUMO and NS2 to simulate the actual VANET scenario and validate the efficiency and performance of our protocol. Simulation results prove the practicability of our protocol for VANETs.