# An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
## 3 IIoT and Privacy-Preserving Architectures
By using blockchain, individuals can maintain control over their personal data and selectively disclose information to third parties, reducing the risk of identity theft and unauthorized data access. Self  
Figure 2: Blockchain scalable architecture for industrial ecosystems [4]. sovereign identity (SSI) solutions, such as uPort and Sovrin, leverage blockchain to enable individuals to manage and control their digital identities, providing privacy-enhancing features and reducing reliance on centralized identity providers. 4. Financial Transactions and Privacy: Blockchain technology can improve privacy in financial transactions by reducing the need for trusted intermediaries and providing pseudonymity. Cryptocurrencies like Bitcoin and privacy-focused cryptocurrencies like Monero utilize blockchain to facilitate secure, decentralized, and pseudonymous transactions. While blockchain transactions are public, privacy-focused techniques such as ring signatures, stealth addresses, and zero-knowledge proofs are employed to obfuscate transaction details and enhance privacy. It is worth noting that while blockchain technology can enhance privacy in these scenarios, its implementation requires careful consideration of the specific use case, including factors such as regulatory compliance, scalability, and user adoption. Federated learning is also a privacy-preserving architecture that enables collaborative machine learning on decentralized data. It allows multiple parties, such as individual devices or edge servers, to train a shared machine learning model without directly sharing their raw data with a central server or each other. Here are some key aspects of federated learning that contribute to its privacy-preserving nature [43; 44]:  
1. Local Training: In federated learning, the training of the machine learning model takes place locally on individual devices or edge servers. This means that data remains on the devices, and only model updates (such as gradients) are shared with the central server or aggregator. 2. Differential Privacy: Differential privacy techniques can be employed in federated learning to further protect privacy. By adding controlled noise or perturbation to the local model updates before sharing them, the individual data points and patterns are obfuscated, preventing the reconstruction of sensitive information. 3. Encryption: Encryption techniques can be applied to protect the confidentiality of the model updates during transmission. Secure multi-party computation (MPC) protocols, homomorphic encryption, or secure enclaves (such as Trusted Execution Environments) can be utilized to ensure that the model updates remain private. 4. Aggregation with Privacy Preservation: The central server or aggregator collects the encrypted or differentially private model updates from the participants and performs the aggregation to update the shared model. Aggregation techniques can be designed in a way that preserves privacy, such as using secure aggregation protocols that do not reveal individual contributions. 5. On-Device Personalization: Federated learning can also support on-device personalization, where the shared model is further fine-tuned or customized on individual devices using locally available data. This approach ensures that sensitive data remains on the user's device, enhancing privacy. 6. Secure Communication: Secure communication protocols, such as encrypted channels and secure socket layers (SSL/TLS), should be employed during data transmission between the participants and the central server to protect against eavesdropping and data tampering. Federated learning allows organizations or individuals to leverage the collective intelligence of decentralized data while minimizing the risks associated with data sharing. This architecture promotes privacy by keeping sensitive data localized, incorporating privacy-preserving algorithms, and utilizing encryption and secure communication protocols.