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We investigate an attack on a machine learning model that predicts whether aperson or household will relocate in the next two years, i.e., apropensity-to-move classifier. The attack assumes that the attacker can querythe model to obtain predictions and that the marginal distribution of the dataon which the model was trained is publicly available. The attack also assumesthat the attacker has obtained the values of non-sensitive attributes for acertain number of target individuals. The objective of the attack is to inferthe values of sensitive attributes for these target individuals. We explore howreplacing the original data with synthetic data when training the model impactshow successfully the attacker can infer sensitive attributes.\footnote{Originalpaper published at PSD 2022. The paper was subsequently updated.}

When Machine Learning Models Leak: An Exploration of Synthetic Training Data