# A Novel Review of Stability Techniques for Improved Privacy-Preserving Machine Learning

arXiv: https://arxiv.org/abs/2406.00073 · HTML: https://mlsanity.com/paper/2406.00073/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 31 May 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Train faster, generalize better: Stability of stochastic gradient descent](https://mlsanity.com/paper/1509.01240/) (2015)
- [NBDT: Neural-Backed Decision Trees](https://mlsanity.com/paper/2004.00221/) (2020)
- [PAC Privacy: Automatic Privacy Measurement and Control of Data Processing](https://mlsanity.com/paper/2210.03458/) (2022)

## Abstract (arXiv)

Machine learning models have recently enjoyed a significant increase in size and popularity. However, this growth has created concerns about dataset privacy. To counteract data leakage, various privacy frameworks guarantee that the output of machine learning models does not compromise their training data. However, this privatization comes at a cost by adding random noise to the training process, which reduces model performance. By making models more resistant to small changes in input and thus more stable, the necessary amount of noise can be decreased while still protecting privacy. This paper investigates various techniques to enhance stability, thereby minimizing the negative effects of privatization in machine learning.