The Price of Ignorance: Information-Free Quotation for Data Retention in Machine Unlearning

2026-04-13

AI Summary

This work addresses the challenge mobile network operators face in balancing model accuracy and retraining costs when complying with users’ right to data deletion. To this end, the authors propose an incremental pricing mechanism that requires no access to users’ private preferences. The server broadcasts progressively increasing prices, enabling users to autonomously decide whether to retain their data. The framework introduces the notion of “ignorance cost” to quantify the welfare loss between this informationally constrained mechanism and the first-best pricing under complete information, proving the gap to be negligible. Relying on subgame-perfect Nash equilibrium analysis, extensive experiments across seven mechanisms and 5,000 Monte Carlo simulations demonstrate that the proposed approach achieves at least 99% of the benchmark social welfare while ensuring robustness to noise and fairness.

Technology Category

• Machine Learning: Privacy
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Data transparency and provenance

Abstract

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 knowledge of users' parameters. Under complete information, the protocol admits a unique subgame-perfect Nash equilibrium characterized by single-period selling. We formalize the Price of Ignorance -- the welfare gap between optimal personalized pricing (which knows everything) and our information-free quotation (which knows nothing) -- and prove a three-regime efficiency ordering. Numerical evaluation across seven mechanisms and 5000 Monte Carlo runs shows that this price is near zero: the information-free mechanism achieves >=99% of the welfare of its information-intensive benchmarks, while providing noise-robust guarantees and comparable fairness.

Problem

• machine unlearning
• data deletion
• privacy preferences
• pricing mechanism
• information asymmetry

Innovation

• machine unlearning
• information-free mechanism
• Price of Ignorance
• data retention pricing
• privacy-preserving incentives