# An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
## 6 Privacy Threats in the IIoT
Grouping vulnerabilities by categories aids in establishing and putting into practice mitigation measures because all ICS deal with information technology (IT) and operational technology (OT). These categories are divided into policy and practice concerns and vulnerabilities found in various platforms (such as hardware, operating systems, and ICS applications). Vulnerabilities can be grouped as Policy Concerns, Practice Concerns, Hardware Vulnerabilities, Operating System Vulnerabilities, ICS Application Vulnerabilities, etc. These categories can help in establishing mitigation measures and prioritizing security efforts [81]. Every ICS environment could have flaws depending on how they are set up and what they are intended to do. A further consideration is an ICS environment's size; the larger the environment, the higher the likelihood that an error could occur. An ICS environment that has modernized its outdated systems and added new technologies like Industrial Internet of Things (IIoT) devices might also have more vulnerabilities that threat actors might take advantage of. Figure 4 depicts a blockchain-based system that combines a multi-signature system and differential privacy algorithms to provide a secure and privacy-preserving solution for a network traffic analysis called BANTA [82; 83]. Figure 4: Use case of BANTA architecture in the ICS network [82]. The BANTA system employs smart contracts to automate network traffic analysis and store network traffic logs in a decentralized manner, resulting in increased transparency and security in the ICS. This multi-signature system requires a specific set of parties to sign off on any changes to the smart contract's behavior or data, preventing rogue administrators from tampering with the smart contract and jeopardizing its security or reliability. In addition, it uses differential privacy algorithms to protect sensitive information in network traffic logs while allowing network administrators to analyze network traffic. The main concerns regarding protecting data privacy in industrial domains are intertwined with the more general concerns about the risks associated with each modern network device [84]. In general, the most basic and common privacy threats related to the IIoT are:  
1. Identification and Authorization. It is directly related to the concept of identity privacy. It refers to the effort to find correlations between the data that can be used to detect, identify, and maliciously replicate the application of profiles (sets of associated data) to personalize and remember secret, industrial information. Techniques such as the Subscriber Identity Module (SIM) and the Machine Identification Module (MIM), proposed by [PERSON] [51], are essential solutions worthy of attention. However, these approaches work in centralized single-management networks. At the same time, it is not easy in distributed topologies to manage identification services and standardizations such as the one proposed by [PERSON] et al. [52], and it concerns an architecture of authorization of remote end-users using distributed smart gateways, which are based on the Datagram Transport Layer Security (DTLS) handshake protocol. In addition, attacks on the IIoT compromise authorized industrial systems access, and as a result, one such security issue can degrade the related services. Ransomware also causes IIoT devices to malfunction and steals users' sensitive information and data. In addition, if many smart IIoT devices cannot encrypt user data, malware will emerge. IIoT devices use a network that does not convert data into code to prevent unauthorized device access. 2. Localization and tracking. It is directly related to the concept of location privacy [85]. An industry can choose the locations that it chooses to perform its economic functions in.