Recursive partitioning and summarization: a practical framework for differentially private data publishing

Abstract

In this paper we consider the problem of differentially private data publishing. In particular, we consider the scenario in which a trusted curator gathers sensitive information from a large number of respondents, creates a relational dataset where each tuple corresponds to one entity, such as an individual, a household, or an organization, and then publishes a privacy-preserving (i.e., sanitized or anonymized) version of the dataset. This has been referred to as the "non-interactive" mode of private data analysis, as opposed to the "interactive" mode, where the data curator provides an interface through which users may pose queries about the data, and get (possibly noisy) answers.