Industrial Internet of Things (IIoT) has emerged as a prospective technology that improves the productivity and automation level for industrial applications. Devices from cooperative IIoT domains will communicate and collaborate on the increasingly complicated manufacturing tasks. To secure cross-domain device collaborations, we propose combining the blockchain with multifactor authentication. Because the multifactor authentication conforms to IIoT devices’ operation modes and brings higher security levels, and the blockchain technology contributes to building trust among different domains. However, this combined usage still has limitations in terms of the potential loss of factor attack, the storage overhead on the blockchain, and the contradiction between efficiency and privacy preservation. Motivated by these facts, in this article, we develop a privacy-preserving blockchain-based multifactor device authentication protocol for cross-domain IIoT. Specifically, multiple factors are additionally encoded by the hardware fingerprint into random numbers, before being transformed into key materials. The blockchain only stores each domain’s dynamic accumulator, which accumulates derived key materials for devices, thereby reducing the overhead. Moreover, the on-chain accumulator is leveraged to efficiently verify the unlinkable identities of cross-domain IIoT devices. The security of our protocol is formally proved, and the security features and functionalities are, respectively, discussed. A proof-of-concept prototype was implemented to prove the efficiency and reliability. The comparison results indicate that the on-chain storage is greatly reduced. Finally, the smart contract’s performance was evaluated to show scalability.

The Internet of Things (IoT) benefits our lives by integrating physical devices to the real world and offers a crucial internet infrastructure for future civilization. Because IoT devices are widely distributed and restricted in resources, it is difficult for them to adopt traditional security methods to resist malicious attacks. Unauthorized access to IoT devices, which results in severe privacy and security problems, has become a major challenge that has impeded IoT technology from being widely adopted. Therefore, the access control for IoT devices urgently needs to be improved when dealing with authorization issues. In this paper, we propose an attribute-based access control scheme that provides decentralized, flexible, and fine-grained authorization for IoT devices. Blockchain is utilized to provide authentic and reliable credentials. More importantly, a verifiable collaboration mechanism is designed to meet the needs of controlled access authorization in emergencies. Authority nodes are constructed to execute major computation tasks and interact with the blockchain. The security analysis shows that our scheme can reliably guarantee the security of authorized access. More than security assurance, a proof-of-concept prototype has been implemented to prove that our scheme is scalable, efficient, and accommodates IoT devices well.

The combination of the Internet of Things (IoT) and cloud-edge (CE) paradigm promises to be an efficient system to aggregate and further process huge volumes of data from IoT nodes. Physical unclonable functions (PUFs) emerge as a prospective primitive to provide IoT nodes with lightweight physical identities for authentication. However, when integrating PUFs into multiserver authentication protocols to improve security, the following problems occur: 1) the challenge–response pairs (CRPs) of PUFs generated by devices need to be explicitly stored by each edge server. This will cause the privacy leakage of CRPs; 2) the reliability is reduced resulting from the single point failure; and 3) existing PUFs-based authentication protocols would need to put great efforts into synchronizing CRPs, to ensure consistency in multiserver systems. To overcome these problems, in this article, we propose a privacy-aware authentication protocol for the multiserver CE-IoT systems by combining PUFs and the blockchain technique. The real correlations of CRPs are double encoded into mapping correlations (MCs) by a one-time physical identity and the keyed-hash function. The blockchain is leveraged to store MCs, synchronize them efficiently, and incorporate the multireceiver encryption to share the physical identity securely. The security of our protocol is formally proved by a random oracle model, and security features are discussed to show that our protocol resists various attacks. Moreover, a prototype was implemented to prove the efficiency of the protocol, and the comparison results present that our protocol accommodates CE-IoT systems. Finally, the simulation of the smart contract evaluates the scalability of our protocol.

Biometric keys are widely used in the digital identity system due to the inherent uniqueness of biometrics. However, existing biometric key generation methods may expose biometric data, which will cause users’ biometric traits to be permanently unavailable in the secure authentication system. To enhance its security and privacy, we propose a secure biometric key generation method based on deep learning in this paper. Firstly, to prevent the information leakage of biometric data, we utilize random binary codes to represent biometric data and adopt a deep learning model to establish the relationship between biometric data and random binary code for each user. Secondly, to protect the privacy and guarantee the revocability of the biometric key, we add a random permutation operation to shuffle the elements of binary code and update a new biometric key. Thirdly, to further enhance the reliability and security of the biometric key, we construct a fuzzy commitment module to generate the helper data without revealing any biometric information during enrollment. Three benchmark datasets including ORL, Extended YaleB, and CMU-PIE are used for evaluation. The experiment results show our scheme achieves a genuine accept rate (GAR) higher than the state-of-the-art methods at a 1% false accept rate (FAR), and meanwhile satisfies the properties of revocability and randomness of biometric keys. The security analyses show that our model can effectively resist information leakage, cross-matching, and other attacks. Moreover, the proposed model is applied to a data encryption scenario in our local computer, which takes less than 0.5 s to complete the whole encryption and decryption at different key lengths.

The authentication of Internet of Things (IoT) devices based on the Physical Unclonable Function (PUF) is widely adopted in the information security domain. However, the leakage of PUF responses in an authentication system reduces its privacy and security. To improve its security, we can utilize the Elliptic Curve Cryptography (ECC) algorithm with different key lengths to encrypt the PUF response arbitrarily. Point multiplication is the most time-consuming operation in ECC because of its complex calculation process, which seriously affects the efficiency of the PUF response encryption. In order to solve this problem, a point multiplier based on binary field with reconfigurable key lengths of 233, 283, 409 and 571 is designed in this paper. In our method, by reusing the underlying computing units, the resources needed for point multiplication are effectively reduced. What it is more innovative is that double point multiplication operations with a key length of less than 283 bits can be performed simultaneously in the elaborate designed point multiplication circuit, which can effectively speed up the encryption process of ECC. The circuit is implemented on Xilinx Virtex-6 FPGA. The experiment results show the single point multiplication times of 233, 283, 409 and 571 key lengths are 19.33, 22.36, 41.36 and 56.5 μs, respectively, under the clock frequency of 135 MHz. In addition, it only needs 19.33 μs to perform two-point multiplication operations when the key length is 233 bits at the same time. When the key length is 283 bits, the point multiplication operation can be performed twice in 22.36 μs.

Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative model training in the Industrial Internet of Things (IIoT). By leveraging the decentralization, immutability, and transparency of blockchain technology, Blockchain-enabled FL (BFL) has gained significant attention for enhancing FL’s security and reliability. However, BFL still faces challenges in motivating client participation. While several incentive mechanisms have been proposed, most primarily focus on short-term rewards and overlook the long-term influence of individual contributions on global model performance. To address these challenges, we propose a novel BFL framework that integrates model training with blockchain mining on the client side. Specifically, we design a long-term incentive mechanism based on repeated game theory, where the interactions between participants and the task publisher (TP) are modeled as an infinitely repeated game. We formally prove the existence of a Subgame Perfect Nash Equilibrium, providing theoretical guarantees for stable long-term cooperation. Furthermore, we introduce a hybrid reward scheme that jointly considers contributions to both training and mining tasks, encouraging sustained engagement and attracting new participants. Extensive experiments on MNIST and CIFAR-10 validate that the proposed mechanism enhances the robustness of FL and effectively promotes long-term client participation.