IBOOST: A Lightweight Provably Secure Identity-Based Online/Offline Signature Technique Based on FCM for Massive Devices in 5G Wireless Sensor Networks

The fifth-generation (5G) wireless network is commercialized. The need to integrate massive devices in 5G and wireless sensor networks (WSN) to provide several convenient services for network users becomes imperative. However, there are growing concerns that 5G-WSNs pose new security threats to sensitive user information. User authentication and key agreement have been provided for secure end-to-end communication. However, stricter security techniques are required as billions of massive devices are being networked to collect and process complex user data in real-time. Therefore, anonymous authentication and authorization are highly coveted for privacy preservation and prevention of unlawful exploitation of user data. However, guaranteeing data integrity, authentication, and non-repudiation require special-purpose identity-based signature techniques that are quite difficult to design in practice. In order to address this problem, this paper proposes a lightweight, provably secure identity-based online/offline signature technique (IBOOST) and its extension for massive devices in 5G-WSNs using fractional chaotic maps. The IBOOST scheme achieves multi-time use of offline storage at a lower processing time. Therefore, the signer can reuse the offline pre-stored information in a polynomial time. This makes our IBOOST superior to the existing online/offline signature techniques that allow only a single signature. Additionally, the new technique enables the pre-registration process with a secret key, and no secret key is required in the offline stage. Also, the proposed IBOOST proves to be secure in the random oracle unforgeability under the chosen message attack (UF-IBS-CMA). Finally, the IBOOST and its enhanced version (A-IBOOST) give the lowest computational costs compared to several contending techniques. Therefore, the proposed IBOOST shows superior security and performance with better computational overhead than the preliminary techniques.