Sharing social network data: differentially private estimation of exponential family random-graph models

Vishesh Karwa, Pavel N. Krivitsky, Aleksandra B. Slavković

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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 analyse synthetic graphs 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 data set 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 analysing 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.

Original languageEnglish (US)
Pages (from-to)481-500
Number of pages20
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume66
Issue number3
DOIs
StatePublished - Apr 1 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Sharing social network data: differentially private estimation of exponential family random-graph models'. Together they form a unique fingerprint.

Cite this