Membership inference

A testing technique, and a category of attack when used adversarially, that determines whether a specific data point was part of a model's training set by analyzing the model's behavior or outputs on that data point compared to data it has never seen. Used defensively, it is how an organization tests whether a model has memorized specific individuals' data. (see Topic 5.3)

An attack that infers whether a specific record was present in a model's training data, a privacy risk distinct from a system revealing a live customer's data in conversation.

A related but distinct attack, covered in the next topic, where an adversary determines whether a specific piece of data was part of a model's training set, exploiting model behavior rather than a retrieval corpus. (see Topic 5.3)

A technique, covered in depth at Topic 5.1, by which a trained model can be queried in a way that reveals whether a specific record was part of its training data, one of the mechanisms behind Vector 4 (model memorization and inference).

A technique for testing whether a specific record was part of a model's training data, typically by exploiting the fact that models behave differently, often with higher confidence, on data they were trained on than on unseen data.