An Efficient and Certificateless Conditional Privacy-Preserving Authentication Scheme for Wireless Body Area Networks Big Data Services

To gurantee the security and privacy of the patient's physiological data in wirelss body area networks (WBANs), it is important to secure the communication between the personal digital assistance held by the WBANs client and the application provider, such as a medical institution, physician, or hospital. These physiological data are so large, traditional methods cannot process them efficiently and securely, thus big data services are needed. In the existing anonymous authentication schemes for WBANs, most of them did not consider when a malicious WBANs client sends a false message to cheat the application provider and cause a medical accident, how to trace the real identity of this client and punish him. In order to overcome the above issues, an efficient and certificateless conditional privacy-preserving authentication scheme for WBANs big data services is proposed in this paper. Due to the proposed scheme is based on big data, the capabilities of the proposed WBANs system is better than traditional WBANs. To improve the performance, the proposed scheme supports batch authentication of multiple clients, which significantly reduces the computational overhead of the application provider. Moreover, the proposed scheme provides anonymity, un-linkability, mutual authentication, traceability, session key establishment, forward secrecy, and attack resistance. The simulation experiment demonstrates that the proposed scheme for WBANs needs less computational time than recent schemes.