Supervised Machine Learning with Plausible Deniability
Sprache des Titels:
We study the question of how well machine learning (ML) models trained on a certain data set provide privacy for the training data or, equivalently, whether it is possible to reverse-engineer the training data from a given ML model. While this is easy to answer negatively in the most general case, it is interesting to note that the protection extends beyond non-recoverability towards plausible deniability: Given a ML model f, we show that one can take a set of purely random training data, and from this define a suitable ?learning rule? that will produce a ML model that is exactly f. Thus, any speculation about which data has been used to train f is deniable upon the claim that any other data could have led to the same results. We corroborate our theoretical finding with practical examples and open source implementations of how to find the learning rules for a chosen set of training data.