# Privacy-preserving Learning via Deep Net Pruning

arXiv: https://arxiv.org/abs/2003.01876 · HTML: https://mlsanity.com/paper/2003.01876/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 12 of 12)

- [Model Sparsity Can Simplify Machine Unlearning](https://mlsanity.com/paper/2304.04934/) (2023)
- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [CaPC Learning: Confidential and Private Collaborative Learning](https://mlsanity.com/paper/2102.05188/) (2021)
- [Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared Randomness](https://mlsanity.com/paper/2306.10015/) (2023)
- [FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity](https://mlsanity.com/paper/2404.09816/) (2024)
- [Fast John Ellipsoid Computation with Differential Privacy Optimization](https://mlsanity.com/paper/2408.06395/) (2024)
- [Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning](https://mlsanity.com/paper/2410.06814/) (2024)
- [Tensorization of neural networks for improved privacy and interpretability](https://mlsanity.com/paper/2501.06300/) (2025)
- [Sparsity in neural networks can improve their privacy](https://mlsanity.com/paper/2304.10553/) (2023)
- [AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks](https://mlsanity.com/paper/2507.06525/) (2025)
- [SoK: Data Minimization in Machine Learning](https://mlsanity.com/paper/2508.10836/) (2025)
- [Private and interpretable clinical prediction with quantum-inspired tensor train models](https://mlsanity.com/paper/2602.06110/) (2026)

## Abstract (arXiv)

This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility. As a first step towards understanding the relationship between neural network pruning and differential privacy, this paper proves that pruning a given layer of the neural network is equivalent to adding a certain amount of differentially private noise to its hidden-layer activations. The paper also presents experimental results to show the practical implications of the theoretical finding and the key parameter values in a simple practical setting. These results show that neural network pruning can be a more effective alternative to adding differentially private noise for neural networks.