Machine learning (ML) is vulnerable to inference (e.g., membership inference, property inference, and data reconstruction) attacks that aim to infer the private information of training data or dataset. Existing defenses are only designed for one specific type of attack and sacrifice significant utility or are soon broken by adaptive attacks. We address these limitations by proposing an information-theoretic defense framework, called Inf2Guard, against the three major types of inference attacks. Our framework, inspired by the success of representation learning, posits that learning shared representations not only saves time/costs but also benefits numerous downstream tasks. Generally, Inf2Guard involves two mutual information objectives, for privacy protection and utility preservation, respectively. Inf2Guard exhibits many merits: it facilitates the design of customized objectives against the specific inference attack; it provides a general defense framework which can treat certain existing defenses as special cases; and importantly, it aids in deriving theoretical results, e.g., inherent utility-privacy tradeoff and guaranteed privacy leakage. Extensive evaluations validate the effectiveness of Inf2Guard for learning privacy-preserving representations against inference attacks and demonstrate the superiority over the baselines.

机器学习中存在多种推断攻击，现有防御方法要么只针对特定类型的攻击且损失很大，要么很快被自适应攻击突破。本研究提出了一种信息理论防御框架Inf2Guard，用于对抗推断攻击。该框架通过学习共享表示来保护隐私和保留效用，并展示了多种优势及对现有防御的改进。实证评估验证了Inf2Guard对于学习对推断攻击具有隐私保护的表示的有效性，并展示了其优于基线方法的卓越性能。

Inf2Guard: 一个信息理论框架用于学习抵抗判断攻击的隐私保护表示