Artificial intelligence, machine learning, and deep learning as a service have become the status quo for many industries, leading to the widespread deployment of models that handle sensitive data. Well-performing models, the industry seeks, usually rely on a large volume of training data. However, the use of such data raises serious privacy concerns due to the potential risks of leaks of highly sensitive information. One prominent threat is the Membership Inference Attack, where adversaries attempt to deduce whether a specific data point was used in a model's training process. An adversary's ability to determine an individual's presence represents a significant privacy threat, especially when related to a group of users sharing sensitive information. Hence, well-designed privacy-preserving machine learning solutions are critically needed in the industry. In this work, we compare the effectiveness of L2 regularization and differential privacy in mitigating Membership Inference Attack risks. Even though regularization techniques like L2 regularization are commonly employed to reduce overfitting, a condition that enhances the effectiveness of Membership Inference Attacks, their impact on mitigating these attacks has not been systematically explored.

翻译：人工智能、机器学习及深度学习即服务已成为众多行业的常态，导致处理敏感数据的模型被广泛部署。业界所追求的高性能模型通常依赖于大量训练数据。然而，由于高度敏感信息存在泄露风险，此类数据的使用引发了严重的隐私担忧。成员推理攻击是当前突出的威胁之一，攻击者试图推断特定数据点是否被用于模型的训练过程。攻击者判定个体数据存在的能力构成了重大隐私威胁，在涉及共享敏感信息的用户群体时尤为严重。因此，业界亟需设计完善的隐私保护机器学习解决方案。本研究对比了L2正则化与差分隐私在缓解成员推理攻击风险方面的有效性。尽管L2正则化等正则化技术常被用于减少过拟合（这种状态会增强成员推理攻击的效果），但其在缓解此类攻击方面的作用尚未得到系统性探索。