Adversarial Machine Unlearning

Understand

This paper focuses on the challenge of machine unlearning, aiming to remove the influence of specific training data on machine learning models.

• Traditionally, the development of unlearning algorithms runs parallel with that of membership inference attacks (MIA), a type of privacy threat to determine whether a data instance was used for training.
• However, the two strands are intimately connected: one can view machine unlearning through the lens of MIA success with respect to removed data.
• Recognizing this connection, we propose a game-theoretic framework that integrates MIAs into the design of unlearning algorithms.