The massive redundant data storage and communication in network 4.0 environments have issues of low integrity, high cost, and easy tampering. To address these issues, a secure data storage and recovery scheme in the blockchain-based network is proposed by improving the decentration, tampering-proof, real-time monitoring and management of storage systems, as such design supports the dynamic storage, fast repair, and update of distributed data in the data storage system of industrial nodes. A local regenerative code technology is used to repair and store data between failed nodes while ensuring the privacy of user data. That is, as the data stored are found to be damaged, multiple local repair groups constructed by vector code can simultaneously yet efficiently repair multiple distributed data storage nodes. Based on the unique chain storage structure, such as data consensus mechanism and smart contract, the storage structure of blockchain distributed coding not only quickly repair the nearby local regenerative codes in the blockchain but also reduce the resource overhead in the data storage process of industrial nodes. Experimental results show that the proposed scheme improves the repair rate of multi-node data by 9% and data storage rate increased by 8.6%, indicating to be promising with good security and real-time performance.

Data credibility plays a key role in facilitating evidence-based decision making in organizations and governments (e.g., policy making). One of the key data sources is the Internet of Things (IoT) devices and systems, say within a fog environment. However, the increasing complexity and interconnectivity of such IoT and fog environments can result in security vulnerabilities (e.g., due to implementation errors or flaws in the underpinning devices or systems), which can be exploited to compromise the credibility of the data. Therefore, in this article, we propose a secure Blockchain-based scheme to guarantee the credibility of nodes and data and ensure data transmission security in the fog environment. We then demonstrate the feasibility of the proposed scheme using experiments.

The integrity of cloud-based convolutional neural network (CNN) prediction services can be jeopardized by a malicious cloud server. Although zero-knowledge proof approaches can be used to verify integrity, they are difficult to use for larger CNN models like LeNet-5 and VGG16, due to the large cost (in terms of time and storage) of generating a proof. This paper proposes ValidCNN, which can efficiently generate integrity proofs based zk-SNARK. At the heart of ValidCNN, it is a novel usage of Freivald's concepts for circuit construction, and a more efficient way for verifying matrix multiplication. Our experimental results demonstrate that VaildCNN significantly outperforms the state-of-the-art approaches that are based on zk-SNARK. For example, compared with ZEN, VaildCNN achieves a 12-fold improvement in time and a 31-fold improvement in storage. Compared with vCNN, VaildCNN achieves a 195-fold and 279-fold improvement in time and storage respectively.