Differential Privacy Made Easy

TL;DR

This paper simplifies the application of differential privacy by reducing tuning parameters, comparing existing Python libraries, and introducing a new user-friendly DP library called GRAM-DP.

Contribution

It minimizes tunable parameters in differential privacy frameworks and presents a new easy-to-use library for non-experts.

Findings

Compared three Python DP libraries for privacy results.

Developed a new simple DP library, GRAM-DP.

Demonstrated ease of use for non-experts in privacy protection.

Abstract

Data privacy is a major issue for many decades, several techniques have been developed to make sure individuals' privacy but still world has seen privacy failures. In 2006, Cynthia Dwork gave the idea of Differential Privacy which gave strong theoretical guarantees for data privacy. Many companies and research institutes developed differential privacy libraries, but in order to get the differentially private results, users have to tune the privacy parameters. In this paper, we minimized these tune-able parameters. The DP-framework is developed which compares the differentially private results of three Python based DP libraries. We also introduced a new very simple DP library (GRAM-DP), so the people with no background of differential privacy can still secure the privacy of the individuals in the dataset while releasing statistical results in public.