2014/09/16 by Karwa, Vishesh, Slavković, Aleksandra B., Krivitsky, Pavel
#Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Methodology (stat.ME) #Other Statistics (stat.OT)
paper · doi:10.48550/arxiv.1409.4696
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.