## 4 A Look Toward Privacy in 6G Core Networks
### Implications of AI for Identity Protections in 6G Technology  
Artificial Intelligence (AI) and Machine Learning (ML) are tools that will be integrated ubiquitously into 6G networks and operations. As these tools require large numbers of data to be trained and will operate in monitoring and system management, there are concerns about how this could affect user identity privacy protections. The researchers who conducted a large study on the effects of ML on privacy in 6G technology present multiple points on how AI and ML could be used by an adversary to erode privacy protections within 6G networks, most notably through Inference of Private Information, Large-Scale Data Collection, and Stenographic Data Generation [59]. The authors mention a core aspect of AI and ML: that it can be a double-edged sword. Takeaways from this observation are that the inclusion of these systems will require the correct people along with strict guidelines in order to protect user privacy. On the other hand, work carried out in [60] brings a positive outlook to privacy protections in 6G networks, revealing how AI systems can be used to help conceal and anonymize user data. A major concern with AI in 6G networks is its possible ability to categorize users by gathering personal and non-personal information. Researchers in [61] bring together their findings in a study on the applicability of privacy solutions discovered against the found issues. Table 4 illustrates their discoveries and conclusions, which bring to light the most important privacy concerns in 6G networks. Table 4 shows the two major issues, similar to those discussed above for 5G technology, with understanding what a private system truly means: it is difficult to resolve defining levelsand indicators for privacy and to determine the border between personal and non-personal data. Table 5 provides a select compilation of recent research publications related to 6G AI applications, with descriptions of each paper and their respective privacy focus areas. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Privacy Solution** & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **N1** & **N2** & **N3** & **N4** & **N5** \\ \hline Non-centralized ML & ✓ & ✓ & ✓ & ✓ & ✓ & X & X & ✓ & ✓ & ✓ & ✓ \\ Differential privacy and data perturbation & ✓ & ✓ & ✓ & X & ✓ & ✓ & ✓ & ✓ & X & ✓ & ✓ \\ Homomorphic encryption & ✓ & ✓ & ✓ & X & ✓ & X & X & ✓ & ✓ & ✓ & ✓ \\ Lightweight cryptography for IoT and edge & ✓ & ✓ & X & ✓ & ✓ & X & X & X & ✓ & ✓ & ✓ \\ Fog computing privacy preservation & ✓ & X & ✓ & ✓ & ✓ & X & X & ✓ & ✓ & ✓ \\ Blockchain-based consensus and storage & ✓ & ✓ & ✓ & ✓ & ✓ & X & X & ✓ & ✓ & ✓ & ✓ \\ Privacy level definitions and quantification & X & X & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & X & ✓ \\ \hline \hline \end{tabular}  
* P1—Privacy attacks on AI models and private data
* P2—IoT (Internet of Things) edge network and edge AI privacy attacks
* P3—Privacy limitations in cloud computing and storage environments
* P4—Cost of privacy enhancements
* P5—Privacy differences based on location
* P6—Difficulty in defining levels and indicators for privacy
* N1—Difficulty determining the border between personal and non-personal data
* N2—Mixing personal data with non-personal data
* N3—Utility vs. privacy of anonymized data
* N4—Privacy attacks on anonymized and synthetic data
* N5—Data Localization Laws vs. free flow of data.  
\end{table} 
Table 4: The privacy solutions and applicability addressing concerns in personal and non-personal data [61]. The privacy solution is labeled in the far-left column, and the abbreviated privacy issues are presented in the columns on the right.