# Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models

arXiv: https://arxiv.org/abs/1511.02930 · HTML: https://mlsanity.com/paper/1511.02930/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2015.

## Built on by (most cited first, 4 of 4)

- [Privacy-Preserving Community Detection for Locally Distributed Multiple Networks](https://mlsanity.com/paper/2306.15709/) (2023)
- [Differentially Private Topological Data Analysis](https://mlsanity.com/paper/2305.03609/) (2023)
- [Signal Recovery from Random Dot-Product Graphs Under Local Differential Privacy](https://mlsanity.com/paper/2504.17274/) (2025)
- [Differentially Private Modeling of Disease Transmission within Human Contact Networks](https://mlsanity.com/paper/2604.07493/) (2026)

## Abstract (arXiv)

Motivated by a real-life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. A case study using a version of the Enron e-mail corpus dataset demonstrates the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy and supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analyzing such data. We use a simple yet effective randomized response mechanism to generate synthetic networks under ε-edge differential privacy, and then use likelihood based inference for missing data and Markov chain Monte Carlo techniques to fit exponential-family random graph models to the generated synthetic networks.