Differentially Private Exponential Random Graphs

  • Vishesh Karwa
  • Aleksandra B. Slavković
  • Pavel Krivitsky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8744)


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.


Exponential random graphs edge differential privacy missing data synthetic graphs 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Vishesh Karwa
    • 1
  • Aleksandra B. Slavković
    • 1
  • Pavel Krivitsky
    • 2
  1. 1.Department of StatisticsThe Pennsylvania State UniversityUSA
  2. 2.School of Mathematics and Applied Statistics of University of WollongongAustralia

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