Mis on liikmelisuse inference rünnak?

Membership inference attack is a type of attack on machine learning models, where an attacker tries to determine whether specific data was included in 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.

Working principle:

• The attacker provides certain data samples as input to 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.

It is important to consider this when developing models, especially in areas with sensitive data (medicine, finance), and to apply protection methods such as differential privacy.