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

TL;DR

This paper introduces a differentially private method for sharing social network data by generating synthetic graphs that protect individual privacy while allowing valid statistical analysis, demonstrated through a case study on the Enron email dataset.

Contribution

It proposes a novel approach combining randomized response and likelihood-based inference to release synthetic social network data with differential privacy guarantees.

Findings

Successfully applied to Enron email data

Maintains data utility for statistical analysis

Ensures privacy of individual relationships

Abstract

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 \emph{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 $\epsilon$-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.