用报价机制让用户自愿留数据，既保护隐私又不损网络性能。
Quotation-Based Data Retention Mechanism for Data Privacy in LLM-Empowered Network Services
- 通过逐轮报价让用户自主决定是否留数据，无需预知隐私偏好。
- 在不降低模型准确率前提下，实现用户数据保留与网络性能平衡。
- 适合关注隐私合规与网络优化协同的运营商和研究者。
将大语言模型（LLMs）用于下一代网络优化，带来新的数据治理挑战。移动网络运营商（MNOs）越来越多地利用生成式AI进行流量预测、异常检测和服务个性化，需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）、《加利福尼亚消费者隐私法》（CCPA）等法规，用户有权撤回同意并要求删除数据。然而，大规模机器遗忘会显著降低模型精度并产生高昂计算成本，最终损害全体用户的网络性能。本文提出一种迭代价格发现机制，使MNOs能通过连续报价补偿用户以保留数据。服务器逐步提高数据保留的单价，用户在每一轮报价中独立决定供应量。该方法无需预先了解用户隐私偏好，可高效最大化网络生态系统的整体福利。
原文摘要 · Abstract (English)
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.
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