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.

翻译：人工智能（AI）的快速发展引发了关于用户数据隐私的担忧，进而催生了《通用数据保护条例》（GDPR）和《加州消费者隐私法案》（CCPA）等法规。借助机器学习遗忘提供的基础工具包，AI服务提供商现在能够从已训练的模型及训练数据集中移除用户数据，以符合此类法规要求。然而，大规模的数据赎回可能成本高昂并降低模型精度。为平衡遗忘成本与隐私保护，我们提出了一种买家发起的数据赎回拍卖机制，使服务提供商能够通过适当补偿从意愿用户处购买数据。该方法无需服务器预先掌握用户的隐私偏好信息，并为所研究问题中的社会福利最大化提供了高效解决方案。