# Differentially Private Exponential Random Graphs

arXiv: https://arxiv.org/abs/1409.4696 · HTML: https://mlsanity.com/paper/1409.4696/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2014.

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

- [Differentially Private Densest Subgraph Detection](https://mlsanity.com/paper/2105.13287/) (2021)
- [FedGES: A Federated Learning Approach for BN Structure Learning](https://mlsanity.com/paper/2502.01538/) (2025)
- [Statistic Selection and MCMC for Differentially Private Bayesian Estimation](https://mlsanity.com/paper/2203.13377/) (2022)
- [Differentially Private Online Bayesian Estimation With Adaptive Truncation](https://mlsanity.com/paper/2301.08202/) (2023)
- [Bayesian Frequency Estimation Under Local Differential Privacy With an Adaptive Randomized Response Mechanism](https://mlsanity.com/paper/2405.07020/) (2024)
- [Differentially Private Modeling of Disease Transmission within Human Contact Networks](https://mlsanity.com/paper/2604.07493/) (2026)
- [Empirical Auditing of Edge-Private Graph Generators](https://mlsanity.com/paper/2609.25155/) (2026)

## Abstract (arXiv)

We propose methods to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network. Proposed techniques aim at fitting and estimating a wide class of exponential random graph models (ERGMs) in a differentially private manner, and thus offer rigorous privacy guarantees. More specifically, we use the randomized response mechanism to release networks under ε-edge differential privacy. To maintain utility for statistical inference, treating the original graph as missing, we propose a way to use likelihood based inference and Markov chain Monte Carlo (MCMC) techniques to fit ERGMs to the produced synthetic networks. We demonstrate the usefulness of the proposed techniques on a real data example.