# Differential Privacy Made Easy

arXiv: https://arxiv.org/abs/2201.00099 · HTML: https://mlsanity.com/paper/2201.00099/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2022.

## Built on by (most cited first, 4 of 4)

- [Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis](https://mlsanity.com/paper/2307.15424/) (2023)
- [Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning](https://mlsanity.com/paper/2409.14280/) (2024)
- [Optimal Variance and Covariance Estimation under Differential Privacy in the Add-Remove Model and Beyond](https://mlsanity.com/paper/2509.04919/) (2025)
- [JANUS: Structured Bidirectional Generation for Guaranteed Constraints and Analytical Uncertainty](https://mlsanity.com/paper/2603.03748/) (2026)

## Abstract (arXiv)

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.