# An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
## 2 Related Work  
The complexity and heterogeneity of IIoT systems make ensuring availability, confidentiality, and integrity difficult. As a result, there is a risk of mistrust in network operations, privacy concerns, and the potential loss of critical personal and sensitive information of the network's end-users. The above challenges have caused widespread concern in the scientific community, prompting several solutions to be proposed at various levels. For example, the existing methods for protecting location privacy are mostly based on traditional anonymization, fuzzy, and cryptography technology, with little success in the big data environment, for example, sensor networks contain sensitive information that must be appropriately protected. Current trends, such as \"Industry 4.0\" and the IIoT, generate, process, and exchange massive amounts of security-critical and privacy-sensitive data, making them appealing targets for cyber-attacks. However, the previous methods ignored the issue of privacy protection, resulting in a violation of privacy. In this paper [5], the authors propose a location privacy protection method that meets the differential privacy constraint while also maximizing the utility of the data and algorithms in the IIoT. Since location data has a high value but a low density, the authors combine utility and privacy to create a multilevel location information tree model. Furthermore, the differential privacy index mechanism is used to select data based on the frequency with which tree nodes access the data. Finally, the Laplace scheme is used to introduce noise into the data accessing frequency. As demonstrated by the theoretical analysis and experimental results, the proposed strategy can significantly improve security, privacy, and applicability. A tensor-based multiple clustering method has recently been created with the goal of uncovering hidden distinct data structures in large data from diverse perspectives, and it may be widely employed in the IIoT to improve production and service quality. Yet, due to the high computational cost and massive volume of data, outsourcing processing to relatively low-cost cloud servers can significantly reduce local costs, but there is a considerable risk of revealing user privacy. To overcome the aforementioned issue, a secure hybrid cloud-based tensor-based multiple clustering method is proposed [6]. The proposed technique encrypts object tensors using a homomorphic cryptosystem and then uses cloud servers to completely conduct various clustering calculations over encrypted object tensors. A set of related security subprotocols is also developed to facilitate privacy-preserving tensor-based multiple clusterings. Just encryption and perturbation removal are performed on the client in the proposed method, making it very lightweight for consumers. Experiment findings show that the suggested approach is accurate and efficient when grouping items into different groups, with no leakage of private or supplementary information. Furthermore, when more cloud nodes are used, the technique offers excellent scalability, making it ideal for clustering Industrial IIoT big data. In addition, clients that are equipped with growing cloud computing choose to outsource an increasing amount of IIoT data to the cloud to alleviate the high storage and processing strain. Existing searchable encryption (SE) systems, however, only apply to IIoT records with textual keyword fields, rather than those with both digital and textual keyword fields. Furthermore, due to the significant key storage expense, the key management issue continues to limit the practicality and availability of SE schemes. To that purpose, the authors [7] describe an outsourced Hybrid Keyword-Field Search over Encrypted Data with Efficient Key Management (HKFS-KM) system that makes use of the relevance score function and a keyed hash tree. A formal security study demonstrates that the HKFS-KM scheme achieves keyword privacy and trapdoor unlinkability in both known ciphertexts and known background attack models.