Explode: An Extensible Platform for Differentially Private Data Analysis

Abstract

Differential privacy (DP) has emerged as a popular standard for privacy protection and received great attention from the research community. However, practitioners often find DP cumbersome to implement, since it requires additional protocols (e.g., for randomized response, noise addition) and changes to existing database systems. To avoid these issues we introduce Explode, a platform for differentially private data analysis. The power of Explode comes from its ease of deployment and use: The data owner can install Explode on top of an SQL server, without modifying any existing components. Explode then hosts a web application that allows users to conveniently perform many popular data analysis tasks through a graphical user interface, e.g., issuing statistical queries, classification, correlation analysis. Explode automatically converts these tasks to collections of SQL queries, and uses the techniques in [3] to determine the right amount of noise that should be added to satisfy DP while producing high utility outputs. This paper describes the current implementation of Explode, together with potential improvements and extensions.