Buyer-Initiated Auction Mechanism for Data Redemption in Machine Unlearning
This work addresses privacy compliance issues for AI service providers under regulations like GDPR and CCPA, offering an incremental solution to manage data redemption efficiently.
The paper tackles the cost and accuracy degradation of extensive data redemption in machine unlearning by proposing a buyer-initiated auction mechanism that allows service providers to purchase data from willing users with compensation, balancing unlearning costs and privacy protection without requiring prior knowledge of user privacy preferences.
The rapid growth of artificial intelligence (AI) has raised privacy concerns over user data, leading to regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). With the essential toolbox provided by machine unlearning, AI service providers are now able to remove user data from their trained models as well as the training datasets, so as to comply with such regulations. However, extensive data redemption can be costly and degrade model accuracy. To balance the cost of unlearning and the privacy protection, we propose a buyer-initiated auction mechanism for data redemption, enabling the service provider to purchase data from willing users with appropriate compensation. This approach does not require the server to have any a priori knowledge about the users' privacy preference, and provides an efficient solution for maximizing the social welfare in the investigated problem.