# Pain-Free Random Differential Privacy with Sensitivity Sampling

arXiv: https://arxiv.org/abs/1706.02562 · HTML: https://mlsanity.com/paper/1706.02562/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2017.

## Built on by (most cited first, 7 of 7)

- [Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning](https://mlsanity.com/paper/2310.05696/) (2023)
- [On the Privacy Risks of Deploying Recurrent Neural Networks in Machine Learning Models](https://mlsanity.com/paper/2110.03054/) (2021)
- [Truthful Generalized Linear Models](https://mlsanity.com/paper/2209.07815/) (2022)
- [Additive Logistic Mechanism for Privacy-Preserving Self-Supervised Learning](https://mlsanity.com/paper/2205.12430/) (2022)
- [DPOAD: Differentially Private Outsourcing of Anomaly Detection through Iterative Sensitivity Learning](https://mlsanity.com/paper/2206.13046/) (2022)
- [DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning](https://mlsanity.com/paper/2309.10965/) (2023)
- [Descend or Rewind? Stochastic Gradient Descent Unlearning](https://mlsanity.com/paper/2511.15983/) (2025)

## Abstract (arXiv)

Popular approaches to differential privacy, such as the Laplace and exponential mechanisms, calibrate randomised smoothing through global sensitivity of the target non-private function. Bounding such sensitivity is often a prohibitively complex analytic calculation. As an alternative, we propose a straightforward sampler for estimating sensitivity of non-private mechanisms. Since our sensitivity estimates hold with high probability, any mechanism that would be (ε,δ)-differentially private under bounded global sensitivity automatically achieves (ε,δ,γ)-random differential privacy (Hall et al., 2012), without any target-specific calculations required. We demonstrate on worked example learners how our usable approach adopts a naturally-relaxed privacy guarantee, while achieving more accurate releases even for non-private functions that are black-box computer programs.