Title

Differentially private exponential random graphs

RIS ID

93559

Publication Details

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

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.

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Link to publisher version (DOI)

http://dx.doi.org/10.1007/978-3-319-11257-2_12