Differential Privacy Made Easy

Abstract

Data privacy has been a significant issue for many decades. Several techniques have been developed to make sure individuals' privacy but still, the 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 differentially private results, users have to tune the privacy parameters. In this paper, we minimized these tunable parameters. The DP-framework is developed which compares the differentially private results of three Python based differential privacy libraries. We also introduced a new very simple DP library (GRAM - DP), so that people with no background in differential privacy can still secure the privacy of the individuals in the dataset while releasing statistical results in public.