Inf2Guard: An Information-Theoretic Framework for Learning
  Privacy-Preserving Representations against Inference Attacks
Sayedeh Leila Noorbakhsh Binghui Zhang Yuan Hong Binghui Wang
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.