2015/11/09 by Vishesh Karwa, Karwa, Vishesh, Pavel N. Krivitsky +3 · 3 citations
Computer Science · Psychology · Social Sciences · #Privacy-Preserving Technologies in Data #LGBTQ Health, Identity, and Policy #Human Mobility and Location-Based Analysis
paper · pdf · doi:10.48550/arxiv.1511.02930
Motivated by a real-life problem of sharing social network data that contain\nsensitive personal information, we propose a novel approach to release and\nanalyze synthetic graphs in order to protect privacy of individual\nrelationships captured by the social network while maintaining the validity of\nstatistical results. A case study using a version of the Enron e-mail corpus\ndataset demonstrates the application and usefulness of the proposed techniques\nin solving the challenging problem of maintaining privacy \and supporting\nopen access to network data to ensure reproducibility of existing studies and\ndiscovering new scientific insights that can be obtained by analyzing such\ndata. We use a simple yet effective randomized response mechanism to generate\nsynthetic networks under \ε-edge differential privacy, and then use\nlikelihood based inference for missing data and Markov chain Monte Carlo\ntechniques to fit exponential-family random graph models to the generated\nsynthetic networks.\n