Glossary
Model Inversion
Model inversion attempts to reconstruct or infer specifics of the data a model was trained on, by analyzing the trained model's own outputs and behavior rather than by accessing the original training dataset directly. Where model extraction targets the model itself — reconstructing its behavior through queries — model inversion targets what went into building it, treating the model as an indirect, imperfect window back onto data that was supposed to remain protected.
This means a model can pose a genuine confidentiality risk to its training data even when the raw dataset itself is stored and access-controlled perfectly, because the risk travels through the model's own learned behavior rather than through direct access to the data it learned from. Protecting the dataset alone doesn't fully address a risk that the trained model itself can independently reintroduce.