46 research outputs found
Protecting Global Properties of Datasets with Distribution Privacy Mechanisms
We consider the problem of ensuring confidentiality of dataset properties
aggregated over many records of a dataset. Such properties can encode sensitive
information, such as trade secrets or demographic data, while involving a
notion of data protection different to the privacy of individual records
typically discussed in the literature. In this work, we demonstrate how a
distribution privacy framework can be applied to formalize such data
confidentiality. We extend the Wasserstein Mechanism from Pufferfish privacy
and the Gaussian Mechanism from attribute privacy to this framework, then
analyze their underlying data assumptions and how they can be relaxed. We then
empirically evaluate the privacy-utility tradeoffs of these mechanisms and
apply them against a practical property inference attack which targets global
properties of datasets. The results show that our mechanisms can indeed reduce
the effectiveness of the attack while providing utility substantially greater
than a crude group differential privacy baseline. Our work thus provides
groundwork for theoretical mechanisms for protecting global properties of
datasets along with their evaluation in practice