# An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
## 3 IIoT and Privacy-Preserving Architectures
It involves embedding privacy features and measures into the architecture, ensuring that privacy is a core principle from the initial design stages. PbD can certainly be applied in IIoT environments. By integrating privacy considerations into the design and development of IIoT systems, organizations can ensure that privacy is a fundamental aspect of their architecture and processes. 2. Differential Privacy [29]: Differential privacy is a technique that aims to protect individual privacy while still allowing useful information to be extracted from datasets. It adds noise or perturbation to the data to prevent the identification of specific individuals while preserving the overall statistical properties of the dataset. Differential privacy can be challenging to implement in IIoT environments due to the decentralized and diverse nature of the data sources. However, with careful design and data aggregation techniques, it is possible to apply differential privacy principles in certain IIoT use cases where data privacy is crucial. 3. Federated Learning [30]: Federated learning is an approach where machine learning models are trained on decentralized data without transferring the data to a central server. This architecture allows for collaborative model training while keeping the data on individual devices, thereby maintaining privacy. Federated learning can be well-suited for IIoT environments, as it allows collaborative model training while keeping the data on individual devices or local servers. This approach preserves privacy by minimizing data transfer and centralization. Figure 1: Privacy and security in the IoT/IIoT using ADVOCATE architecture [27]. Homomorphic Encryption [31]: Homomorphic encryption enables computation on encrypted data without decrypting it. It allows data to be processed securely in an encrypted state, preserving privacy during computation. Homomorphic encryption can be complex to implement in resource-constrained IIoT devices and systems due to its computational overhead. However, advancements in hardware and cryptographic techniques may make it more feasible for specific IIoT use cases where privacy-preserving computations are necessary. 5. Zero-Knowledge Proofs [32]: Zero-knowledge proofs are cryptographic protocols that allow one party to prove the validity of certain information to another party without revealing the actual information. This approach enables the verification of data or statements without exposing the underlying sensitive data. Zero-knowledge proofs can be challenging to implement in IIoT environments due to the limited computational capabilities and communication constraints of IIoT devices. However, they can be applied in certain scenarios where privacy-preserving authentication or verification is required. 6. Data Minimization [33]: Data minimization involves collecting and retaining only the necessary data for a specific purpose, reducing the exposure of personal information. By limiting the amount of data collected, processed, and stored, privacy risks are reduced. Data minimization is highly relevant and applicable in IIoT environments. Limiting the collection, processing, and retention of personal data to what is strictly necessary helps reduce privacy risks and ensures compliance with privacy regulations. 7. User-centric Identity and Access Management (IAM) [34]: User-centric IAM puts individuals in control of their personal information. It allows users to manage their own identity and control the sharing of their personal data, ensuring privacy preferences are respected. User-centric IAM may have limited applicability in IIoT environments since the concept of individual users may not always align with the industrial setting. However, similar principles can be applied to manage access, authentication, and authorization of IIoT devices and systems, ensuring that privacy preferences are respected. These privacy-preserving architectures aim to strike a balance between the need to collect and process data for functional purposes while respecting individual privacy rights.