# Correlated Data in Differential Privacy: Definition and Analysis

arXiv: https://arxiv.org/abs/2008.00180 · HTML: https://mlsanity.com/paper/2008.00180/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2020.

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

- [Managing Correlations in Data and Privacy Demand](https://mlsanity.com/paper/2509.02856/) (2025)
- [Differentially Private Learning of Hawkes Processes](https://mlsanity.com/paper/2207.13741/) (2022)
- [Nonparametric spectral density estimation using interactive mechanisms under local differential privacy](https://mlsanity.com/paper/2504.00919/) (2025)
- [Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls](https://mlsanity.com/paper/2308.06895/) (2023)
- [DP-SNP-TIHMM: Differentially Private, Time-Inhomogeneous Hidden Markov Models for Synthesizing Genome-Wide Association Datasets](https://mlsanity.com/paper/2510.05777/) (2025)
- [Correlated-Sequence Differential Privacy](https://mlsanity.com/paper/2511.18025/) (2025)
- [Integrating Feature Correlation in Differential Privacy with Applications in DP-ERM](https://mlsanity.com/paper/2605.03945/) (2026)

## Abstract (arXiv)

Differential privacy is a rigorous mathematical framework for evaluating and protecting data privacy. In most existing studies, there is a vulnerable assumption that records in a dataset are independent when differential privacy is applied. However, in real-world datasets, records are likely to be correlated, which may lead to unexpected data leakage. In this survey, we investigate the issue of privacy loss due to data correlation under differential privacy models. Roughly, we classify existing literature into three lines: 1) using parameters to describe data correlation in differential privacy, 2) using models to describe data correlation in differential privacy, and 3) describing data correlation based on the framework of Pufferfish. Firstly, a detailed example is given to illustrate the issue of privacy leakage on correlated data in real scenes. Then our main work is to analyze and compare these methods, and evaluate situations that these diverse studies are applied. Finally, we propose some future challenges on correlated differential privacy.