Model Extraction Oriented Data Publishing with k anonymity
Machine learning with personal data has been successfully employed in recent years. Protecting the privacy of a given personal dataset with utilities is therefore a major subject within the privacy community. The classifier trained on an anonymized dataset typically has poorer generalization performance than the classifier trained on the original dataset. We adopt the concept of Tramer et al.'s Model Extraction attack to place models trained on anonymized datasets similar to the original model. This approach also prevents the target variable from being published as it is. Using three open datasets, we do experiments to determine how close the original model is to the model trained on a dataset constructed by our method. Particularly when using Nursery Data set, the model trained on our anonymized dataset with (k 20\ anonymity gives a model that has almost the same (95\ predictions on a given test dataset as the original model.
IWSEC