Definition
Membership inference is a type of privacy attack in artificial intelligence where an attacker attempts to determine if a specific data record was part of the dataset used to train a machine learning model. This attack exploits the fact that models often behave differently when presented with data they were trained on versus unseen data. Understanding membership inference is crucial for protecting sensitive information within AI systems and ensuring compliance with data privacy regulations. For instance, an attacker could use membership inference to deduce if a particular patient's medical record was included in a hospital's diagnostic AI model. This differs from model inversion attacks, which aim to reconstruct training data rather than just confirm its presence. Knowledge of membership inference is relevant for cybersecurity certifications like CompTIA Security+ and CySA+.