Emerging network computing technologies extend the functionalities of industrial IoT (IIoT) terminals. However, this promising service-provisioning scheme encounters problems in untrusted and distributed IIoT scenarios because malicious service providers or clients may deny service provisions or usage for their own interests. Traditional nonrepudiation solutions fade in IIoT environments due to requirements of trusted third parties or unacceptable overheads. Fortunately, the blockchain revolution facilitates innovative solutions. In this paper, we propose a blockchain-based fair nonrepudiation service provisioning scheme for IIoT scenarios in which the blockchain is used as a service publisher and an evidence recorder. Each service is separately delivered via on-chain and off-chain channels with mandatory evidence submissions for nonrepudiation purpose. Moreover, a homomorphic-hash-based service verification method is designed that can function with mere on-chain evidence. And an impartial smart contract is implemented to resolve disputes. The security analysis demonstrates the dependability, and the evaluations reveal the effectiveness and efficiency.

Since network storage services achieve widespread adoption, security and performance issues are becoming primary concerns, affecting the scalability of storage systems. Countermeasures like data auditing mechanisms and deduplication techniques are widely studied. However, the existing data auditing mechanism with deduplication cannot solve the problems such as high cost and reliance on trusted third parties in traditional approaches, and it also faces the problem of repeated auditing of data shared by multiple-tenant. This article proposes a blockchain-based deduplicatable data auditing mechanism. We first design a client-side data deduplication scheme based on bilinear-pair techniques to reduce the burden on users and service providers. On this basis, we achieve a trustworthy and efficient data auditing mechanism that helps to check data integrity by using both the blockchain technique and bilinear pairing cryptosystem. The blockchain system is used to record the behaviors of entities in both data outsourcing and auditing processes so that the corresponding immutable records can be used to not only ensure the credibility of audit results but also help to monitor unreliable third-party auditors. Finally, theoretical analysis and experiments reveal the effectiveness and performance of our scheme.

Network storage services have benefited countless users worldwide due to the notable features of convenience, economy and high availability. Since a single service provider is not always reliable enough, more complex multi-cloud storage systems are developed for mitigating the data corruption risk. While a data auditing scheme is still needed in multi-cloud storage to help users confirm the integrity of their outsourced data. Unfortunately, most of the corresponding schemes rely on trusted institutions such as the centralized third-party auditor (TPA) and the cloud service organizer, and it is difficult to identify malicious service providers after service disputes. Therefore, we present a blockchain-based multi-cloud storage data auditing scheme to protect data integrity and accurately arbitrate service disputes. We not only introduce the blockchain to record the interactions among users, service providers, and organizers in data auditing process as evidence, but also employ the smart contract to detect service dispute, so as to enforce the untrusted organizer to honestly identify malicious service providers. We also use the blockchain network and homomorphic verifiable tags to achieve the low-cost batch verification without TPA. Theoretical analyses and experiments reveal that the scheme is effective in multi-cloud environments and the cost is acceptable.

The maturity of network storage technology drives users to outsource local data to remote servers. Since storage service providers are not reliable enough for keeping users' data, remote data auditing mechanisms are studied for mitigating the threat to data integrity. However, many traditional schemes achieve verifiable data integrity for users only without resolutions to data possession disputes, while others depend on centralized third-party auditors (TPAs) for credible arbitrations. Recently, the emergence of blockchain technology promotes inspiring countermeasures. In this paper, we propose a decentralized arbitrable remote data auditing scheme for network storage services based on blockchain techniques. We use a smart contract to notarize integrity metadata of outsourced data recognized by users and service providers on the blockchain, and also utilize the blockchain network as the self-recording channel for achieving non-repudiation verification interactions. We also propose a fairly arbitrable data auditing protocol with the support of the commutative hash technique, defending against dishonest provers and verifiers. Additionally, a decentralized adjudication mechanism is implemented by using the smart contract technique for creditably resolving data possession disputes without TPAs. The theoretical analysis and experimental evaluation reveal its effectiveness in undisputable data auditing, and the limited requirement of costs.

A trusted routing scheme is very important to ensure the routing security and efficiency of wireless sensor networks (WSNs). There are a lot of studies on improving the trustworthiness between routing nodes, using cryptographic systems, trust management, or centralized routing decisions, etc. However, most of the routing schemes are difficult to achieve in actual situations as it is difficult to dynamically identify the untrusted behaviors of routing nodes. Meanwhile, there is still no effective way to prevent malicious node attacks. In view of these problems, this paper proposes a trusted routing scheme using blockchain and reinforcement learning to improve the routing security and efficiency for WSNs. The feasible routing scheme is given for obtaining routing information of routing nodes on the blockchain, which makes the routing information traceable and impossible to tamper with. The reinforcement learning model is used to help routing nodes dynamically select more trusted and efficient routing links. From the experimental results, we can find that even in the routing environment with 50% malicious nodes, our routing scheme still has a good delay performance compared with other routing algorithms. The performance indicators such as energy consumption and throughput also show that our scheme is feasible and effective.

While the Industrial Internet of Things (IIoT) brings convenience to industry, it also brings security problems. Due to the massive amount of data generated by the surge of IIoT devices, it is impossible to ensure whether these data contain attack or untrustworthy data, so how to ensure the security and trustworthiness of IIoT devices becomes an urgent problem to be solved. Here, we design a new hinge classification algorithm based on mini-batch gradient descent with an adaptive learning rate and momentum (HCA-MBGDALRM) to minimize the effects of security attacks. The algorithm significantly improves the performance of deep network training compared with traditional neural networks, decision trees and logistic regression in terms of scale and speed. In addition, we have solved the data skew problem in the shuffle phase, and we implement a parallel framework for HCA-MBGDALRM to accelerate the processing speed of very large traffic datasets.