Differential Privacy Made Easy
This work addresses the usability barrier in differential privacy for practitioners, making it more accessible for non-experts to apply privacy protections, though it is incremental in simplifying existing methods.
The paper tackles the complexity of tuning privacy parameters in differential privacy implementations by developing a framework that compares three Python DP libraries and introduces a new simple library (GRAM-DP), enabling users without expertise to secure data privacy while releasing statistical results.
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.