# Quotation-Based Data Retention Mechanism for Data Privacy in LLM-Empowered Network Services

arXiv: https://arxiv.org/abs/2503.23001 · HTML: https://mlsanity.com/paper/2503.23001/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 29 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions](https://mlsanity.com/paper/2412.19823/) (2024)
- [Machine Unlearning: A Survey](https://mlsanity.com/paper/2306.03558/) (2023)
- [A Survey of Data Pricing for Data Marketplaces](https://mlsanity.com/paper/2303.04810/) (2023)
- [Online Pricing with Reserve Price Constraint for Personal Data Markets](https://mlsanity.com/paper/1911.12598/) (2019)
- [Price-Discrimination Game for Distributed Resource Management in Federated Learning](https://mlsanity.com/paper/2308.13838/) (2023)

## Abstract (arXiv)

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.