Consider a data publishing setting for a data set with public and private features. The objective of the publisher is to maximize the amount of information about the public features in a revealed data set, while keeping the information leaked about the private features bounded. In this talk, we introduce and analyze the utility cost of privacy mechanisms designed to account for distribution estimation errors, therefore providing robust privacy guarantees.