# Differential Privacy Overview and Fundamental Techniques

arXiv: https://arxiv.org/abs/2411.04710 · HTML: https://mlsanity.com/paper/2411.04710/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising](https://mlsanity.com/paper/1805.06530/) (2018)
- [Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey](https://mlsanity.com/paper/2202.08187/) (2022)
- [Census TopDown: The Impacts of Differential Privacy on Redistricting](https://mlsanity.com/paper/2203.05085/) (2022)

## Built on by (most cited first, 1 of 1)

- [BinaryShield: Cross-Service Threat Intelligence in LLM Services using Privacy-Preserving Fingerprints](https://mlsanity.com/paper/2509.05608/) (2025)

## Abstract (arXiv)

This chapter is meant to be part of the book "Differential Privacy in Artificial Intelligence: From Theory to Practice" and provides an introduction to Differential Privacy. It starts by illustrating various attempts to protect data privacy, emphasizing where and why they failed, and providing the key desiderata of a robust privacy definition. It then defines the key actors, tasks, and scopes that make up the domain of privacy-preserving data analysis. Following that, it formalizes the definition of Differential Privacy and its inherent properties, including composition, post-processing immunity, and group privacy. The chapter also reviews the basic techniques and mechanisms commonly used to implement Differential Privacy in its pure and approximate forms.