Membership Inference Privacy

Membership inference privacy (MIP) focuses on assessing and mitigating the risk that machine learning models reveal whether specific data points were used in their training. Current research emphasizes developing more accurate and efficient methods for measuring this privacy leakage, often focusing on likelihood-ratio based attacks and exploring connections to established privacy frameworks like differential privacy. This work aims to provide stronger, more interpretable privacy guarantees while minimizing the impact on model utility, impacting the responsible deployment of machine learning in sensitive domains like healthcare and finance. Improved MIP metrics and techniques are crucial for balancing the benefits of machine learning with the need for robust data protection.