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Differentially private exponential random graphs

Journal Article


Abstract


  • 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.

Authors


  •   Karwa, Vishesh (external author)
  •   Slavkovic, Aleksandra B. (external author)
  •   Krivitsky, Pavel N.

Publication Date


  • 2014

Citation


  • Karwa, V., Slavkovic, A. B. & Krivitsky, P. (2014). Differentially private exponential random graphs. Lecture Notes in Computer Science, 8744 143-155.

Scopus Eid


  • 2-s2.0-84949141014

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3245

Has Global Citation Frequency


Number Of Pages


  • 12

Start Page


  • 143

End Page


  • 155

Volume


  • 8744

Place Of Publication


  • Germany

Abstract


  • 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.

Authors


  •   Karwa, Vishesh (external author)
  •   Slavkovic, Aleksandra B. (external author)
  •   Krivitsky, Pavel N.

Publication Date


  • 2014

Citation


  • Karwa, V., Slavkovic, A. B. & Krivitsky, P. (2014). Differentially private exponential random graphs. Lecture Notes in Computer Science, 8744 143-155.

Scopus Eid


  • 2-s2.0-84949141014

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3245

Has Global Citation Frequency


Number Of Pages


  • 12

Start Page


  • 143

End Page


  • 155

Volume


  • 8744

Place Of Publication


  • Germany