As 6G evolves into an AI-native technology, the integration of artificial intelligence (AI) and Generative AI into cellular communication systems presents unparalleled opportunities for enhancing connectivity, network optimization, and personalized services. However, these advancements also introduce significant data protection challenges, as AI models increasingly depend on vast amounts of personal data for training and decision-making. In this context, ensuring compliance with stringent data protection regulations, such as the General Data Protection Regulation (GDPR), becomes critical for the design and operational integrity of 6G networks. These regulations shape key system architecture aspects, including transparency, accountability, fairness, bias mitigation, and data security. This paper identifies and examines the primary data protection risks associated with AI-driven 6G networks, focusing on the complex data flows and processing activities throughout the 6G lifecycle. By exploring these risks, we provide a comprehensive analysis of the potential privacy implications and propose effective mitigation strategies. Our findings stress the necessity of embedding privacy-by-design and privacy-by-default principles in the development of 6G standards to ensure both regulatory compliance and the protection of individual rights.

翻译：随着6G技术向AI原生范式演进，人工智能与生成式AI在蜂窝通信系统中的深度融合，为增强连接性、优化网络性能及提供个性化服务带来了前所未有的机遇。然而，这些进步也引发了严峻的数据保护挑战，因为AI模型日益依赖海量个人数据进行训练与决策。在此背景下，确保符合《通用数据保护条例》等严格的数据保护法规，对于6G网络的设计与运营完整性至关重要。这些法规影响着系统架构的关键层面，包括透明度、问责制、公平性、偏见缓解及数据安全。本文系统识别并剖析了AI驱动型6G网络面临的主要数据保护风险，重点关注6G全生命周期中复杂的数据流与处理活动。通过深入探究这些风险，我们对潜在的隐私影响进行了全面分析，并提出了有效的缓解策略。研究结果强调，必须在6G标准制定中嵌入"隐私保护设计"与"默认隐私保护"原则，以确保既符合监管要求，又能切实保障个人权利。