What is a membership inference attack?

Membership inference attack — is a type of attack on machine learning models where an attacker tries to determine whether specific data was part of the model's training set.

Such an attack can be used to reveal confidential information, for example, if the model was trained on private user data.

How it works:

• The attacker inputs certain data samples into the model.
• Analyzes the model's responses (e.g., class probabilities).
• Based on these responses, attempts to determine whether these samples were in the training set.

This is important to consider when developing models, especially in fields with sensitive data (medicine, finance), and to apply protective methods such as differential privacy.