# Differential Privacy Regularization: Protecting Training Data Through Loss Function Regularization

arXiv: https://arxiv.org/abs/2409.17144 · HTML: https://mlsanity.com/paper/2409.17144/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Differential Privacy Has Disparate Impact on Model Accuracy](https://mlsanity.com/paper/1905.12101/) (2019)
- [Security and Privacy Challenges of Large Language Models: A Survey](https://mlsanity.com/paper/2402.00888/) (2024)
- [A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems](https://mlsanity.com/paper/2402.18649/) (2024)
- [Privately Fine-Tuning Large Language Models with Differential Privacy](https://mlsanity.com/paper/2210.15042/) (2022)
- [Fine-Tuning Large Language Models with User-Level Differential Privacy](https://mlsanity.com/paper/2407.07737/) (2024)
- [On the utility and protection of optimization with differential privacy and classic regularization techniques](https://mlsanity.com/paper/2209.03175/) (2022)

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

- [Differential Privacy: Gradient Leakage Attacks in Federated Learning Environments](https://mlsanity.com/paper/2510.23931/) (2025)

## Abstract (arXiv)

Training machine learning models based on neural networks requires large datasets, which may contain sensitive information. The models, however, should not expose private information from these datasets. Differentially private SGD [DP-SGD] requires the modification of the standard stochastic gradient descent [SGD] algorithm for training new models. In this short paper, a novel regularization strategy is proposed to achieve the same goal in a more efficient manner.