The deployment of large language models (LLMs) for next-generation network optimization introduces novel data governance challenges. mobile network operators (MNOs) increasingly leverage generative artificial intelligence (AI) for traffic prediction, anomaly detection, and service personalization, requiring access to users' sensitive network usage data-including mobility patterns, traffic types, and location histories. Under the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and similar regulations, users retain the right to withdraw consent and demand data deletion. However, extensive machine unlearning degrades model accuracy and incurs substantial computational costs, ultimately harming network performance for all users. We propose an iterative price discovery mechanism enabling MNOs to compensate users for data retention through sequential price quotations. The server progressively raises the unit price for retaining data while users independently determine their supply at each quoted price. This approach requires no prior knowledge of users' privacy preferences and efficiently maximizes social welfare across the network ecosystem.

翻译：大型语言模型（LLM）在下一代网络优化中的部署带来了新的数据治理挑战。移动网络运营商（MNO）日益利用生成式人工智能（AI）进行流量预测、异常检测和服务个性化，这需要访问用户的敏感网络使用数据——包括移动模式、流量类型和位置历史。根据《通用数据保护条例》（GDPR）、《加州消费者隐私法案》（CCPA）及类似法规，用户保留撤回同意并要求删除数据的权利。然而，大规模的机器遗忘会降低模型准确性并产生大量计算成本，最终损害所有用户的网络性能。我们提出一种迭代价格发现机制，使MNO能够通过连续报价补偿用户以保留数据。服务器逐步提高保留数据的单位报价，同时用户在各报价点独立决定其数据供给量。该方法无需预先了解用户的隐私偏好，并能有效实现网络生态系统整体社会福利的最大化。