# Differential Privacy in Machine Learning: A Survey from Symbolic AI to LLMs

arXiv: https://arxiv.org/abs/2506.11687 · HTML: https://mlsanity.com/paper/2506.11687/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 58 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [Deep Double Descent: Where Bigger Models and More Data Hurt](https://mlsanity.com/paper/1912.02292/) (2019)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy](https://mlsanity.com/paper/2303.00654/) (2023)
- [Minimax Optimal Procedures for Locally Private Estimation](https://mlsanity.com/paper/1604.02390/) (2016)
- [Winning the NIST Contest: A scalable and general approach to differentially private synthetic data](https://mlsanity.com/paper/2108.04978/) (2021)
- [Differential Privacy for Text Analytics via Natural Text Sanitization](https://mlsanity.com/paper/2106.01221/) (2021)
- [Selective Differential Privacy for Language Modeling](https://mlsanity.com/paper/2108.12944/) (2021)
- [ADePT: Auto-encoder based Differentially Private Text Transformation](https://mlsanity.com/paper/2102.01502/) (2021)
- [Towards Practical Differential Privacy for SQL Queries](https://mlsanity.com/paper/1706.09479/) (2017)
- [Differentially Private Random Decision Forests using Smooth Sensitivity](https://mlsanity.com/paper/1606.03572/) (2016)
- [Automatic Discovery of Privacy-Utility Pareto Fronts](https://mlsanity.com/paper/1905.10862/) (2019)
- [OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization](https://mlsanity.com/paper/2210.01318/) (2022)
- [Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey](https://mlsanity.com/paper/2309.16398/) (2023)
- [Post-processing of Differentially Private Data: A Fairness Perspective](https://mlsanity.com/paper/2201.09425/) (2022)
- [DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges](https://mlsanity.com/paper/2106.12949/) (2021)
- [Deconvoluting Kernel Density Estimation and Regression for Locally Differentially Private Data](https://mlsanity.com/paper/2008.12466/) (2020)

## Abstract (arXiv)

Machine learning models should not reveal particular information that is not otherwise accessible. Differential privacy provides a formal framework to mitigate privacy risks by ensuring that the inclusion or exclusion of any single data point does not significantly alter the output of an algorithm, thus limiting the exposure of private information. This survey reviews the foundational definitions of differential privacy and traces their evolution through key theoretical and applied contributions. It then provides an in-depth examination of how DP has been integrated into machine learning models, analyzing existing proposals and methods to preserve privacy when training ML models. Finally, it describes how DP-based ML techniques can be evaluated in practice. By offering a comprehensive overview of differential privacy in machine learning, this work aims to contribute to the ongoing development of secure and responsible AI systems.