# R'enyi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration

arXiv: https://arxiv.org/abs/2312.13985 · HTML: https://mlsanity.com/paper/2312.13985/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds](https://mlsanity.com/paper/1605.02065/) (2016)
- [Hyperparameter Tuning with Renyi Differential Privacy](https://mlsanity.com/paper/2110.03620/) (2021)
- [Privacy Amplification by Iteration](https://mlsanity.com/paper/1808.06651/) (2018)
- [Wasserstein Dependency Measure for Representation Learning](https://mlsanity.com/paper/1903.11780/) (2019)
- [Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss](https://mlsanity.com/paper/2205.13710/) (2022)
- [On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them](https://mlsanity.com/paper/2006.08403/) (2020)
- [Faster high-accuracy log-concave sampling via algorithmic warm starts](https://mlsanity.com/paper/2302.10249/) (2023)
- [Investigating Membership Inference Attacks under Data Dependencies](https://mlsanity.com/paper/2010.12112/) (2020)
- [Attribute Privacy: Framework and Mechanisms](https://mlsanity.com/paper/2009.04013/) (2020)
- [Pufferfish Privacy Mechanisms for Correlated Data](https://mlsanity.com/paper/1603.03977/) (2016)
- [Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies](https://mlsanity.com/paper/1312.3913/) (2013)
- [Protecting Global Properties of Datasets with Distribution Privacy Mechanisms](https://mlsanity.com/paper/2207.08367/) (2022)
- [Local Obfuscation Mechanisms for Hiding Probability Distributions](https://mlsanity.com/paper/1812.00939/) (2018)
- [Kantorovich Mechanism for Pufferfish Privacy](https://mlsanity.com/paper/2201.07388/) (2022)

## Built on by (most cited first, 2 of 2)

- [Provable Privacy with Non-Private Pre-Processing](https://mlsanity.com/paper/2403.13041/) (2024)
- [Learning with Differentially Private (Sliced) Wasserstein Gradients](https://mlsanity.com/paper/2502.01701/) (2025)

## Abstract (arXiv)

Pufferfish privacy is a flexible generalization of differential privacy that allows to model arbitrary secrets and adversary's prior knowledge about the data. Unfortunately, designing general and tractable Pufferfish mechanisms that do not compromise utility is challenging. Furthermore, this framework does not provide the composition guarantees needed for a direct use in iterative machine learning algorithms. To mitigate these issues, we introduce a R'enyi divergence-based variant of Pufferfish and show that it allows us to extend the applicability of the Pufferfish framework. We first generalize the Wasserstein mechanism to cover a wide range of noise distributions and introduce several ways to improve its utility. We also derive stronger guarantees against out-of-distribution adversaries. Finally, as an alternative to composition, we prove privacy amplification results for contractive noisy iterations and showcase the first use of Pufferfish in private convex optimization. A common ingredient underlying our results is the use and extension of shift reduction lemmas.