With the extensive use of machine learning technologies, data providers encounter increasing privacy risks. Recent legislation, such as GDPR, obligates organizations to remove requested data and its influence from a trained model. Machine unlearning is an emerging technique designed to enable machine learning models to erase users' private information. Although several efficient machine unlearning schemes have been proposed, these methods still have limitations. First, removing the contributions of partial data may lead to model performance degradation. Second, discrepancies between the original and generated unlearned models can be exploited by attackers to obtain target sample's information, resulting in additional privacy leakage risks. To address above challenges, we proposed a game-theoretic machine unlearning algorithm that simulates the competitive relationship between unlearning performance and privacy protection. This algorithm comprises unlearning and privacy modules. The unlearning module possesses a loss function composed of model distance and classification error, which is used to derive the optimal strategy. The privacy module aims to make it difficult for an attacker to infer membership information from the unlearned data, thereby reducing the privacy leakage risk during the unlearning process. Additionally, the experimental results on real-world datasets demonstrate that this game-theoretic unlearning algorithm's effectiveness and its ability to generate an unlearned model with a performance similar to that of the retrained one while mitigating extra privacy leakage risks.

翻译：随着机器学习技术的广泛应用，数据提供者面临的隐私风险日益增加。GDPR等近期立法要求组织从训练模型中删除请求数据及其影响。机器遗忘是一种新兴技术，旨在使机器学习模型能够擦除用户的私有信息。尽管已有多种高效的机器遗忘方案被提出，但这些方法仍存在局限性。首先，移除部分数据的贡献可能导致模型性能下降。其次，原始模型与生成遗忘模型之间的差异可能被攻击者利用以获取目标样本信息，从而引发额外的隐私泄露风险。为解决上述挑战，我们提出了一种博弈论机器遗忘算法，该算法模拟了遗忘性能与隐私保护之间的竞争关系。该算法包含遗忘模块和隐私模块：遗忘模块具有由模型距离和分类误差构成的损失函数，用于推导最优策略；隐私模块旨在使攻击者难以从遗忘数据中推断成员信息，从而降低遗忘过程中的隐私泄露风险。此外，在真实数据集上的实验结果表明，该博弈论遗忘算法能有效生成与重新训练模型性能相近的遗忘模型，同时缓解额外隐私泄露风险。