# An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
## 4 Dimensions of Privacy  
Privacy in the industrial sector is a concept that is very difficult to define, especially in the digital age of the IIoT, where the convergence of services creates unclear boundaries of definition. Depending on the contexts, relationships, and products involved, it can have many connotations. To properly design privacy settings for IIoT architectures, technologists \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Technology** & **Description** & **Privacy Benefits** & **Challenges** & **Comparison** \\ \hline \hline \multirow{9}{*}{Federated Learning} & \multirow{9}{*}{Data Localization: Federated learning allows data to remain on local devices or servers, reducing the risk of data increases or unauthorized access.} & Model Heterogeneity: Federated learning can be challenging when dealing with a diverse range of edge devices or servers with different computational capabilities, data distributions, or data quality. & Data Handling: Federated learning keeps the data locally and only exchanges model updates or aggregated information. \\ \cline{2-4}  & & & Central Server Trust: While federated learning aims to preserve privacy, it still requires trust in the central server that aggregates model updates. A compromised or malicious server could potentially extract sensitive information from the updates. & Data Privacy: Federated learning focuses on preserving the privacy of individual data points during model training. \\ \cline{2-4}  & & & Model Interpretability and Debugging: Federated learning can make it challenging to interpret and debug models trained across multiple devices or edge nodes. & Trust Model: Federated learning relies on trust in the central server and the integrity of participants. \\ \cline{2-4}  & & & Identifying erroneous contributions, or diagnosing the root causes of failures may require specialized techniques and tools. & \\ \hline \hline \end{tabular}
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Table 2: _Cont._must research and understand the dimensions of privacy that are important to the users of the services in question [27]. Privacy, as derived from E.U. directives and based on the way it is described by [PERSON] et al. [50], can be categorized as or take the following dimensions:  
1. Identity Privacy. It concerns the identity details of an entity and is related to the concepts of authentication and authorization. Most IIoT data is designed for usage by restricted user groups [51]. Consequently, authentication (understanding the identity of the node or user) and authorization (by granting the necessary access rights) are critical, especially regarding copyright, patents, etc., which are crucial to the existence and viability of the industry [52]. 2. Location Privacy. It refers to an entity's location identification information. Said determination violates personal or industrial privacy issues concerning the detection, identification, storage, processing, and sharing of information in a technical or legal context [53]. 3. Footprint Privacy. It refers to an entity's unique traceable communication actions. A feature of this function can be found in smart energy grids, characterized by real-time two-way communications [54]. The difficulty of controlling and retrieving energy data exchanged with third parties threatens network users' privacy. The proposed approach is a comprehensive solution for overcoming concerns related to [PERSON] et al. The standard of footprint's privacy allows a home user to save encrypted measurement data on a cloud server. When financial audits are required, an authorized requester can submit two queries to the cloud server to retrieve the measurement data [55]. 4. Multidimensional Privacy. It refers to an entity's multidimensional or complex identification element, which may combine some of the above dimensions [50]. Solving such problems requires complex integrated processes or solving and parameterizing the individual issues in a custom schema [56].