Knowledge Cross-Distillation for Membership Privacy

Understand

A membership inference attack (MIA) poses privacy risks for the training data of a machine learning model.

• With an MIA, an attacker guesses if the target data are a member of the training dataset.
• The state-of-the-art defense against MIAs, distillation for membership privacy (DMP), requires not only private data for protection but a large amount of unlabeled public data.
• However, in certain privacy-sensitive domains, such as medicine and finance, the availability of public data is not guaranteed.