# Three Tools for Practical Differential Privacy

arXiv: https://arxiv.org/abs/1812.02890 · HTML: https://mlsanity.com/paper/1812.02890/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2018.

## Built on by (most cited first, 20 of 20)

- [Mixed Differential Privacy in Computer Vision](https://mlsanity.com/paper/2203.11481/) (2022)
- [On Privacy and Personalization in Cross-Silo Federated Learning](https://mlsanity.com/paper/2206.07902/) (2022)
- [Differentially Private Learning with Per-Sample Adaptive Clipping](https://mlsanity.com/paper/2212.00328/) (2022)
- [Practical Differentially Private Hyperparameter Tuning with Subsampling](https://mlsanity.com/paper/2301.11989/) (2023)
- [Gradient Leakage Attack Resilient Deep Learning](https://mlsanity.com/paper/2112.13178/) (2021)
- [Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise](https://mlsanity.com/paper/2211.15893/) (2022)
- [DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release](https://mlsanity.com/paper/2311.14056/) (2023)
- [Architecture Matters: Investigating the Influence of Differential Privacy on Neural Network Design](https://mlsanity.com/paper/2111.14924/) (2021)
- [Efficient Hyperparameter Optimization for Differentially Private Deep Learning](https://mlsanity.com/paper/2108.03888/) (2021)
- [Can LLMs be Fooled? Investigating Vulnerabilities in LLMs](https://mlsanity.com/paper/2407.20529/) (2024)
- [On the Importance of Architecture and Feature Selection in Differentially Private Machine Learning](https://mlsanity.com/paper/2205.06720/) (2022)
- [Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent](https://mlsanity.com/paper/2307.11939/) (2023)
- [Coupling public and private gradient provably helps optimization](https://mlsanity.com/paper/2310.01304/) (2023)
- [Empirical Privacy Variance](https://mlsanity.com/paper/2503.12314/) (2025)
- [How Much User Context Do We Need? Privacy by Design in Mental Health NLP Application](https://mlsanity.com/paper/2209.02022/) (2022)
- [R+R:Understanding Hyperparameter Effects in DP-SGD](https://mlsanity.com/paper/2411.02051/) (2024)
- [Enhancing DP-SGD through Non-monotonous Adaptive Scaling Gradient Weight](https://mlsanity.com/paper/2411.03059/) (2024)
- [NanoBatch Privacy: Enabling fast Differentially Private learning on the IPU](https://mlsanity.com/paper/2109.12191/) (2021)
- [Exploring Machine Learning Privacy/Utility trade-off from a hyperparameters Lens](https://mlsanity.com/paper/2303.01819/) (2023)
- [Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)](https://mlsanity.com/paper/2508.15141/) (2025)

## Abstract (arXiv)

Differentially private learning on real-world data poses challenges for standard machine learning practice: privacy guarantees are difficult to interpret, hyperparameter tuning on private data reduces the privacy budget, and ad-hoc privacy attacks are often required to test model privacy. We introduce three tools to make differentially private machine learning more practical: (1) simple sanity checks which can be carried out in a centralized manner before training, (2) an adaptive clipping bound to reduce the effective number of tuneable privacy parameters, and (3) we show that large-batch training improves model performance.