Reconstructing Training Data with Informed Adversaries. (arXiv:2201.04845v1 [cs.CR])

Given access to a machine learning model, can an adversary reconstruct the
model's training data? This work studies this question from the lens of a
powerful informed adversary who knows all the training data points except one.
By instantiating concrete attacks, we show it is feasible to reconstruct the
remaining data point in this stringent threat model. For convex models (e.g.
logistic regression), reconstruction attacks are simple and can be derived in
closed-form. For more general models (e.g. neural networks), …

adversaries data training