Protecting Global Properties of Datasets with Distribution Privacy Mechanisms. (arXiv:2207.08367v2 [cs.CR] UPDATED)

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 …

confidentiality data data protection datasets distribution framework global information literature privacy problem protecting protection secrets sensitive information trade work