Membership inference attacks detect data used to train machine learning models
One of the wonders of machine learning is that it turns any kind of data into mathematical equations. Once you train a machine learning model on training examples—whether it’s on images, audio, raw text, or tabular data—what you get is a set of numerical parameters. In most cases, the model no longer needs the training dataset and uses the tuned parameters to map new and unseen examples to categories or value predictions.
You can then discard the training data and publish the model on GitHub or run it on your own servers without worrying about storing or distributing sensitive information contained in the training dataset.
