Personal Data Protection in AI-Native 6G Systems

• URL: http://arxiv.org/abs/2411.03368v1
• Date: Tue, 05 Nov 2024 10:35:04 GMT
• Title: Personal Data Protection in AI-Native 6G Systems
• Authors: Keivan Navaie,
• Abstract summary: We examine the primary data protection risks associated with AI-driven 6G networks, focusing on the complex data flows and processing activities. Our findings stress the necessity of embedding privacy-by-design and privacy-by-default principles in the development of 6G standards.
• Score: 3.2688512759172195
• License:
• Abstract: 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.

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