The Price of Ignorance: Information-Free Quotation for Data Retention in Machine Unlearning
When users exercise data deletion rights under the General Data Protection Regulation (GDPR) and similar regulations, mobile network operators face a tradeoff: excessive machine unlearning degrades model accuracy and incurs retraining costs, yet existing pricing mechanisms for data retention require the server to know every user's private privacy and accuracy preferences, which is infeasible under the very regulations that motivate unlearning. We ask: what is the welfare cost of operating without this private information? We design an information-free ascending quotation mechanism where the server broadcasts progressively higher prices and users self-select their data supply, requiring no...
| Authors | Bin Han, Di Feng, Zexin Fang et al. |
|---|---|
| Primary category | cs.GT |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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