# Privacy Framework for the Development of IoT-Based Systems
## 6 Proposal of Privacy Guidelines
### Proposal
#### 6.3.3 Proposal Part 3: Automated Privacy Enforcement in IoTS  
In dynamic and resource-constrained IoT situations, automatic privacy enforcement is a viable approach to privacy management. Without requiring human interaction, it helps guarantee data protection by modifying privacy settings in real-time circumstances, such as user behavior or network conditions. This method improves system privacy overall and lessens user strain. Given the wide range of IoT application domains, from smart homes to healthcare and industrial systems, the conventional manual configuration of privacy settings becomes inadequate and prone to errors. Automated privacy, frequently powered by AI, ML, or context-aware rule-based systems, allows privacy protections to be adjusted in real time according to contextual variables including network status, device behavior, and user presence [46]. In this section, we explore the automated privacy enforcement and highlight key approaches and technologies that enable its implementation. _AI-Based Privacy Preservation_  
The growing emphasis on privacy in IoT systems has led to the increased use of ML techniques to support privacy-preserving solutions. ML can detect usage patterns, identify anomalies, and recommend privacy settings based on past user behavior. Moreover, it enables adaptive responses to frequent contextual changes by aligning them with user privacy preferences [47]. Building on this, [PERSON] et al. [30] emphasized the importance of ML and DL in detecting IoT botnets, which represent a critical threat to both privacy and security. ML \begin{table}
\begin{tabular}{l l l} \hline \hline
**IoT Domain** & **Recommended Algorithm** & **Performance/Rational** \\ \hline Smart Home & PICCOLO, SIMON, & Low memory and \\  & SPECK, TWINE & processing requirements \\ RFID/Logistics & SPECK, PICCOLO, SIMON & Limited space, no power \\ Smart Agriculture & TWINE, SIMON, SPECK, & Energy efficiency, minimal \\  & PRESENT & processing, remote \\  & deployment \\ Healthcare & SIMON, SPECK, & Privacy, real-time response, \\  & PICCOLO, PRESENT, & low resources \\  & MIDORI & Wireless communication, \\ Industrial Systems & MIDORI, PRINCE & hard-to-access sensors \\
5G World & PRINCE, PRESENT, & Secure real-time \\  & SIMON, MIDORI & communication \\ Remote Keyless Entry & KEELOQ & Secure lightweight cipher \\  & & for cars and buildings \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recommended lightweight cryptographic algorithms for different IoT application domains. algorithms can be trained to recognize patterns in network traffic or device behavior that may indicate a botnet attack, and they can automatically respond to such threats and protect users' privacy. However, while these techniques are powerful, they also raise privacy concerns, particularly when large volumes of sensitive data are involved. Therefore, there is a need for privacy-preserving ML and DL models that can operate on encrypted or decentralized data, thereby avoiding the direct exposure of personal information. FL has been highlighted by [PERSON] et al. [31] as a promising solution for privacy preservation. FL decentralizes data processing, allowing IoT devices to collaboratively train models while keeping all training data local. This mitigates privacy risks and reduces transmission overheads, aligning well with privacy-by-design principles. Furthermore, advancements in communication technologies--such as 5G and emerging 6G networks--offer high bandwidth, ultra-low latency, and edge computing capabilities. These developments enable IoT devices to utilize local edge resources to train and execute ML models more efficiently and privately. As highlighted by [PERSON] et al. [48], both FL and edge learning paradigms show strong potential in addressing both scalability and privacy limitations inherent in centralized approaches. Thus, adopting adaptive AI methods is recommended to enhance privacy in IoT environments. However, further research is needed to ensure their efficiency and accuracy under real-world constraints.