Wireless sensor networks (WSNs) play an important role in the Industrial Internet of Things (IIoT), and have been widely used in many industrial fields to gather data of monitoring area. However, due to the open nature of wireless channel and resource-constrained feature of sensor nodes, how to guarantee that the sensitive sensor data can only accessed by valid user becomes an key challenge in IIoT environment. Some user authentication protocols for WSNs have been proposed to address this issue. However, previous more or less have their own weaknesses, such as not providing user anonymity and other ideal functions, or being vulnerable to some attacks. To provide secure communication for IIoT, a user authentication protocol scheme with privacy protection for IIoT has been proposed. The security of the proposed scheme is proved under random oracle model, and other security discussions show that the proposed protocol is robust to various attacks. Furthermore, the comparison results with other related protocols and the simulation by NS-3 show that the proposed protocol is secure and efficient for IIoT.

Edge intelligent computing is widely used in the fields, such as the Internet of Medical Things (IoMT) and industrial control UAV clusters, which has advantages, including high data processing efficiency, strong real-time performance and low network delay. However, there are many problems including privacy disclosure, limited calculation force when edge intelligent devices, edge gateways and clouds complete the task unloading, as well as scheduling and coordination issues. Federated learning allows all training devices to complete training at the same time, which greatly improves training efficiency. However, traditional federated learning will expose patient's privacy information of the training set. Due to the sensitive nature of the healthcare data, the aforementioned approach of transferring the patient's data to the central servers may create serious security and privacy issues. Therefore, this article proposes a Privacy Protection Scheme for Federated Learning under Edge Computing (PPFLEC). First of all, we propose a lightweight privacy protection protocol based on a shared secret and weight mask, which is based on a random mask scheme of secret sharing. It is more accurate and efficient than federated learning for secure multiparty computing frameworks based on homomorphic encryption. It can not only protect gradient privacy without losing model accuracy, but also resist equipment dropping and collusion attacks between devices. Second, we design an algorithm based on a digital signature and hash function, which achieves the \textcolor{blue}{integrity and consistency} of the message, as well as resisting replay attacks. Finally, we propose a periodic average training strategy, compared with differential privacy to prove that our scheme is 40% faster in efficiency than in deferential privacy. Meanwhile, compared with federated learning, we can achieve the same efficiency under the condition of ensuring safety. Therefore, our scheme can work well in unstable edge computing environments such as smart healthcare.

In recent years, two technologies, the cloud computing, and the Internet of Things (IoT) have a synergistic effect in the modern organizations as digitization is a new business trend for various industries. Therefore, many organizations outsource their crowdsourced Industrial-IoT (IIoT) data in the cloud system to reduce data management overhead. However, data authentication is one of the fundamental security/trust requirements in such IIoT network. Certificateless signature (CLS) scheme is a cryptographic primitive that provides data authenticity in IIoT systems. Recently, CLS has become a prime research focus due to its ability to solve the key-escrow problem in very recent identity-based signature technique. Many CLS schemes have already been developed using map-to-point (MTP) hash function and random oracle model (ROM). However, due to the implementation difficulty and probabilistic nature of MTP function and ROM, those CLSs are impractical. Hence, the development of a CLS for lightweight devices mounted in IIoT has become one of the most focused research trends. This paper presents a new pairing-based CLS scheme without MTP function and ROM. The new CLS is secure against both the Type-I and Type-II adversaries under the hardness of Extended Bilinear Strong Diffie-Hellman (EBSDH) and Bilinear Strong Diffie-Hellman (BSDH) assumptions, respectively. Performance evaluation and comparison proves that our scheme outperforms other CLS schemes.

Nowadays, the Internet of Things (IoT) and cloud computing have become more pervasive in the context of the industry as digitization becomes a business priority for various organizations. Therefore, industries outsource their crowdsourced Industrial IoT (IIoT) data in the cloud in order to reduce the cost for sharing data and computation. However, the privacy of such crowdsourced data in this environment has attracted wide attention across the globe. Signcryption is the significant cryptographic primitive that meets both the requirement of authenticity and confidentiality of crowdsourced data among users/industries, and thus, it is ideal for ensuring secure authentic data storage and transmission in industrial crowdsourcing environments. In this work, we introduce a new identity-based signcryption (IBSC) scheme using bilinear pairing for IIoT deployment. Besides, two hard problems are studied, called as, modified bilinear Diffie-Hellman inversion (MBDHI) assumption and modified bilinear strong Diffie-Hellman (MBSDH) assumption. The rigorous security analysis demonstrates that our IBSC scheme for IIoT is provably secure based on the intractability of decisional-MBDHI and MBSDH assumptions under formal security model without considering the concept of the random oracle. The performance comparison with other signcryption schemes shows satisfactory results. Thus, our IBSC scheme is appropriate for IIoT crowdsourcing environments, and also applicable for low-bandwidth communications.