# An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
## 2 Related Work
The experimental findings utilizing real-world datasets demonstrate its efficiency and applicability in practice. Although cyber-attacks on the Industrial Internet of Things (IIoT) continue to be a serious concern, blockchain has emerged as a viable technology for IIoT security due to its decentralization and immutability. Current blockchain designs, on the other hand, provide considerable computational complexity and latency concerns, making them unsuitable for the IIoT. Xyreum, a novel high-performance and scalable blockchain for increased IIoT security and privacy, is proposed in this research [8]. To accomplish Mutual Multi-Factor Authentication, Xyreum employs a Time-based Zero-Knowledge Proof of Knowledge (T-ZKPK) with authenticated encryption (MMFA). T-ZKPK characteristics are also utilized to help with Key Establishment for transaction security. Their approach to establishing consensus, which is a group decision-making process on the blockchain, is based on lightweight cryptographic techniques. They test their scheme for security, privacy, and performance, and the results show that, when compared to existing relevant blockchain solutions, their scheme is secure, privacy-preserving, and achieves a significant reduction in computation complexity and latency performance while maintaining high scalability. They also demonstrate a blockchain-based security protocol used in a variety of application domains. Moreover, deep learning offers a great chance to extract usable knowledge from the massive amounts of data in the IIoT. Yet, the absence of large public datasets will result in poor performance and overfitting of the learned model. As a result, federated deep learning across distant datasets has been proposed. Yet, it invariably brings new security challenges, such as revealing participant data privacy. Existing solutions, however, cannot ensure the privacy of each participant's data in a learning group. The authors [9] suggest two privacy-preserving asynchronous deep learning systems in this article: DeepPAR (privacy-preserving and asynchronous deep learning via re-encryption) and DeepDPA (dynamic privacy-preserving and asynchronous deep learning). In comparison to previous work, DeepPAR secures each participant's input privacy while maintaining dynamic update secrecy naturally. Meanwhile, DeepDPA allows for the backward privacy of group participants to be guaranteed in a lightweight manner. Security analyses and performance tests on real-world datasets demonstrate that their suggested systems are safe, efficient, and effective. On the other hand, the rapid increase in the volume of data created by the connected devices in the IIoT paradigm brings up new opportunities for improving service quality for developing applications through data sharing. Yet, data providers have significant challenges in sharing their data through wireless networks due to security and privacy concerns (e.g., data leakage). Private data leaks can cause major problems beyond financial loss for companies. The authors [10] first design a blockchain-powered safe data-sharing architecture for the multiple distributed parties in this study. Finally, using privacy-protected federated learning, they transform the data-sharing challenge into a machine-learning problem. Data privacy is protected by providing the data model rather than releasing the actual data. Lastly, they incorporate federated learning into the permissioned blockchain consensus process, such that the computing work for the consensus can also be used for federated training. The suggested data-sharing strategy achieves good accuracy, high efficiency, and better security, according to the numerical results generated from the real-world datasets. Finally, the advantage of using edge computing is that it may be used as a complement to cloud computing; blockchain is an alternative for creating a transparent safe environment for data storage/governance.