Quotation-Based Data Retention Mechanism for Data Privacy in LLM-Empowered Network Services
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914468096638976 |
|---|---|
| author | Han, Bin Feng, Di Fang, Zexin Wang, Jie Schotten, Hans D. |
| author_facet | Han, Bin Feng, Di Fang, Zexin Wang, Jie Schotten, Hans D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23001 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quotation-Based Data Retention Mechanism for Data Privacy in LLM-Empowered Network Services Han, Bin Feng, Di Fang, Zexin Wang, Jie Schotten, Hans D. Machine Learning Computer Science and Game Theory 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. |
| title | Quotation-Based Data Retention Mechanism for Data Privacy in LLM-Empowered Network Services |
| topic | Machine Learning Computer Science and Game Theory |
| url | https://arxiv.org/abs/2503.23001 |