The data generated in the Industrial Internet of Things (IIoT) has important research value. In the process of data sharing, data privacy, security, and data availability are important issues that cannot be ignored. This paper proposes a blockchain privacy protection scheme based on zero‐knowledge proof to realize the secure sharing of data among data owners, cloud service providers, and semitrusted cloud servers. First, the method of combining zero‐knowledge proof and smart contract is used to verify the availability of data between the data owner and the cloud service provider under the premise of protecting data privacy. Second, proxy reencryption technology is used to realize the secure sharing of data among authorized cloud service providers. In addition, data sharing transaction information between multiple parties and data hashes with digital signatures are stored on the blockchain to achieve public and verifiable data sharing information and data validity. Finally, the theoretical analysis of the scheme shows that the scheme meets the confidentiality requirements of security, integrity, and validity.

Digital systems are changing to security systems in contemporary days. It is time for the digital system to have sufficient security to defend against threats and attacks. The intrusion detection system can identify an anomaly from an external or internal source in the network system. Many kinds of threats are present, that is, active and passive. These dangers could lead to anomalies in the system by which data can be attacked and taken by attackers from the beginning to the destination. Machine learning nowadays is a developing topic; its applications are wide. We can forecast the future through machine learning and classify the right class. In this paper, we employed the new binary and multiclass classification model of Convolutional Neural Networks (CNNs) to identify the anomaly of the network system. In this respect, we used the NSLKDD dataset. Our model uses a Convolutional Neural Network (CNN) to conduct binary and multiclass classification. In both datasets, we build a DL-based DoS detection model. We focus on the DoS category in the most extensively used IDS dataset, KDD. As the name implies, CNN is the most extensively used the DL model for image recognition. Adding a pooling layer to the convolution layer minimizes the size of the feature data extracted from the image while maintaining I/O and spatial information. The CNN model has shown the promising results of multiclass and binary classification in terms of validation loss of 0.0012 at 11th epochs and validation accuracy of 98% and 99%, respectively.

The authenticity and integrity of healthcare is the primary objective. Numerous reversible watermarking schemes have been developed to improve the primary objective but increasing the quantity of embedding data leads to covering image distortion and visual quality resulting in data security detection. A trade-off between robustness, imperceptibility, and embedded capacity is difficult to achieve with current algorithms due to limitations in their ability. Keeping this purpose insight, an improved reversibility watermarking methodology is proposed to maximize data embedding capacity and imperceptibility while maintaining data security as a primary concern. A key is generated by a random path with minimum bit flipping is selected in the 4 × 4 block to gain access to the data embedding patterns. The random path’s complex structure ensures data security. Data of various sizes (8 KB, 16 KB, 32 KB) are used to analyze image imperceptibility and evaluate quality factors. The proposed reversible watermarking methodology performance is tested under standard structures PSNR, SSIM, and MSE. The results revealed that the MRI watermarked images are imperceptible, like the cover image when LSB is 3 bits plane. Our proposed reversible watermarking methodology outperforms other related techniques in terms of average PSNR (49.29). Experiment results show that the suggested reversible watermarking method improves data embedding capacity and imperceptibility compared to existing state-of-the-art approaches.

The network resource allocation in SDN for control applications is becoming a key problem in the near future because of the conflict between the need of the flow-level flexibility control and the limited capacity of flow table. Based on the analysis of the difference of the definition of network resource between SDN and traditional IP network, the idea of the integrated allocation of link bandwidth and flow table for multiple control applications in SDN is proposed in this paper. Furthermore, a price-based joint allocation model of network resource in SDN is built by introducing the price for each of the resources, which can get the proportional fair allocation of link bandwidth and the minimum global delay at the same time. We have also designed a popular flow scheduling policy based on the proportional fair allocation of link bandwidth in order to achieve the minimum global delay. A flow scheduling module has been implemented and evaluated in Floodlight, named virtual forwarding space (VFS). VFS can not only implement the fair allocation of link bandwidth and minimum delay flow scheduling in data plane but also accelerate packet forwarding by looking up flow cache in control plane.

Blockchain is the supporting technology for cryptocurrencies, which can be used to record transaction information among users. However, the information on blockchain can be accessed and verified by any user. To some extent, it is effortless to acquire users' identities, transaction privacy information by means of transaction tracing technology and data analysis technology. In order to tackle the above problems, we proposed a transaction privacy enhancement scheme based on the computational Diffie‐Hellman problem with provable security. By leveraging a group signature scheme, the mixing peer can mix the transactions so that the transaction sender can be hidden into a mixing group. In addition, our solution improves the efficiency by utilizing a signature aggregation scheme and reducing the overhead of the signature verification. The security analysis shows that the proposed scheme is superior in privacy and the efficiency of signature verification.