## 4 A Look Toward Privacy in 6G Core Networks
### Trustworthiness in 6G Networks  
As discussed previously, trust in the serving cellular network is a major factor that must be considered by consumers and operators alike. Trustworthiness in 6G technology ranges across many different topics regarding AI, along with user privacy and identity ramifications. The work carried out in [70] shares how trustworthiness is a multidisciplinary topic covering technology, regulation, techno-economics, politics, and ethics. Pertaining to privacy, the authors state that blockchain technologies can aid in 6G technologies while offering immutability, transparency, verifiability, anonymity, and pseudonyms in data sharing mechanisms, authentication, and network access control. FL is also noted as a privacy-focused method for training larger 6G ML models. Ethical aspects are also presented as a vital focus for a user's trust in the network as 6G technology plans to bring more cyber-physical systems into the cellular ecosystem [71]. The ethics of data privacy is noted as an ever more important topic around the world, as standards creators and vendors must consider how data are processed and protected to uphold the rights of users. The \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Current 6G AI Privacy Literature**} \\ \hline
**Paper Title and Citation** & **Focus** & **Description** \\ \hline ‘Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions’ [65] & Cellular network evolution, self-learning AI systems & Details how 6G technology can be used to create smart cities with interconnected services and applications. Stresses the importance of data privacy in the use of self-learning edge AI systems and data caching. \\ \hline  & & An operational framework is presented named TrustGAIN, which aims to protect against AI poising or prompt attacks, ensuring fair and unbiased output and maintaining user privacy with encryption. The authors also share how differential privacy can be used to limit privacy leaks but note that it cannot eliminate them. Homomorphic encryption on user datasets is also mentioned, adding identity protections and processing encrypted data without the need for decryption. \\ \hline ‘On Security Strategies for Addressing Potential Vulnerabilities in 6G Technologies Deployable in Healthcare’ [67] & Healthcare privacy & Reviews current and past privacy and security concerns in cellular networks, such as unauthorized access and data breaches. Proposes Zero-Trust Architectures (ZTA) with the use of AI to enforce strict privacy measures for medical data. \\ \hline  & & The authors cover the expected aspects of agentic LLMs in 6G networks with a split approach for on-device capabilities and the use of edge computing. User privacy is discussed as a split approach with associated pros and cons: high anonymity when using on-device model inference with concerns arising from the reliance on edge computing for complex tasks. The authors note that privacy in edge computing poses a risk in the case of malicious edge nodes. \\ \hline  & & This research covers an exhaustive list of XAI use cases in 6G technology, ranging from self-driving vehicles to medicine. Important privacy focuses include leveraging XAI to catch user privacy ‘leakages’, understand re-identification attacks on user datasets, and retain user expectations in terms of service capabilities and performance. It is prominent that XAI is still a bleeding-edge field with open research questions, notably, Can privacy threats be posed by the increased transparency of AI decision-making? \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._authors raise open questions regarding automated or AI systems and where liability will be placed during malfunctions that could directly affect customers. Figure 5 outlines the core aspects of network trustworthiness in 6G technology, and the need for further privacy and identity protections.