# Pufferfish Privacy Mechanisms for Correlated Data

arXiv: https://arxiv.org/abs/1603.03977 · HTML: https://mlsanity.com/paper/1603.03977/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2016.

## Built on by (most cited first, 16 of 16)

- [Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis](https://mlsanity.com/paper/2111.01361/) (2021)
- [Unleashing the Power of Randomization in Auditing Differentially Private ML](https://mlsanity.com/paper/2305.18447/) (2023)
- [Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications](https://mlsanity.com/paper/2101.04039/) (2021)
- [Inferring Sensitive Attributes from Model Explanations](https://mlsanity.com/paper/2208.09967/) (2022)
- [Protecting Global Properties of Datasets with Distribution Privacy Mechanisms](https://mlsanity.com/paper/2207.08367/) (2022)
- [Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems](https://mlsanity.com/paper/2306.13054/) (2023)
- [R'enyi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration](https://mlsanity.com/paper/2312.13985/) (2023)
- [Provable Privacy with Non-Private Pre-Processing](https://mlsanity.com/paper/2403.13041/) (2024)
- [Location Trace Privacy Under Conditional Priors](https://mlsanity.com/paper/2102.11955/) (2021)
- [Differentially Private Learning of Hawkes Processes](https://mlsanity.com/paper/2207.13741/) (2022)
- [On Privacy and Confidentiality of Communications in Organizational Graphs](https://mlsanity.com/paper/2105.13418/) (2021)
- [A Shuffling Framework for Local Differential Privacy](https://mlsanity.com/paper/2106.06603/) (2021)
- [Differentially Private Publication of Electricity Time Series Data in Smart Grids](https://mlsanity.com/paper/2408.16017/) (2024)
- [Power Mechanism: Private Tabular Representation Release for Model Agnostic Consumption](https://mlsanity.com/paper/2510.05581/) (2025)
- [Integrating Feature Correlation in Differential Privacy with Applications in DP-ERM](https://mlsanity.com/paper/2605.03945/) (2026)
- [Masking Radar Cognition under Adversarial Surveillance: A Distributional Privacy Framework](https://mlsanity.com/paper/2609.07428/) (2026)

## Abstract (arXiv)

Many modern databases include personal and sensitive correlated data, such as private information on users connected together in a social network, and measurements of physical activity of single subjects across time. However, differential privacy, the current gold standard in data privacy, does not adequately address privacy issues in this kind of data. This work looks at a recent generalization of differential privacy, called Pufferfish, that can be used to address privacy in correlated data. The main challenge in applying Pufferfish is a lack of suitable mechanisms. We provide the first mechanism -- the Wasserstein Mechanism -- which applies to any general Pufferfish framework. Since this mechanism may be computationally inefficient, we provide an additional mechanism that applies to some practical cases such as physical activity measurements across time, and is computationally efficient. Our experimental evaluations indicate that this mechanism provides privacy and utility for synthetic as well as real data in two separate domains.