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We consider the problem of ensuring confidentiality of dataset propertiesaggregated over many records of a dataset. Such properties can encode sensitiveinformation, such as trade secrets or demographic data, while involving anotion of data protection different to the privacy of individual recordstypically discussed in the literature. In this work, we demonstrate how adistribution privacy framework can be applied to formalize such dataconfidentiality. We extend the Wasserstein Mechanism from Pufferfish privacyand the Gaussian Mechanism from attribute privacy to this framework, thenanalyze their underlying data assumptions and how they can be relaxed. We thenempirically evaluate the privacy-utility tradeoffs of these mechanisms andapply them against a practical property inference attack which targets globalproperties of datasets. The results show that our mechanisms can indeed reducethe effectiveness of the attack while providing utility substantially greaterthan a crude group differential privacy baseline. Our work thus providesgroundwork for theoretical mechanisms for protecting global properties ofdatasets along with their evaluation in practice.

Protecting Global Properties of Datasets with Distribution Privacy Mechanisms