Differentially private exponential random graphs

Vishesh Karwa, Aleksandra B. Slavković, Pavel Krivitsky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsJosep Domingo-Ferrer
PublisherSpringer Verlag
Pages143-155
Number of pages13
ISBN (Electronic)9783319112565
StatePublished - Jan 1 2014
EventInternational Conference on Privacy in Statistical Databases, PSD 2014 - Ibiza, Spain
Duration: Sep 17 2014Sep 19 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8744
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Conference on Privacy in Statistical Databases, PSD 2014
CountrySpain
CityIbiza
Period9/17/149/19/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Karwa, V., Slavković, A. B., & Krivitsky, P. (2014). Differentially private exponential random graphs. In J. Domingo-Ferrer (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 143-155). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8744). Springer Verlag.