19,206 research outputs found
Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models
Motivated by a real-life problem of sharing social network data that contain
sensitive personal information, we propose a novel approach to release and
analyze synthetic graphs in order to protect privacy of individual
relationships captured by the social network while maintaining the validity of
statistical results. A case study using a version of the Enron e-mail corpus
dataset demonstrates the application and usefulness of the proposed techniques
in solving the challenging problem of maintaining privacy \emph{and} supporting
open access to network data to ensure reproducibility of existing studies and
discovering new scientific insights that can be obtained by analyzing such
data. We use a simple yet effective randomized response mechanism to generate
synthetic networks under -edge differential privacy, and then use
likelihood based inference for missing data and Markov chain Monte Carlo
techniques to fit exponential-family random graph models to the generated
synthetic networks.Comment: Updated, 39 page
Differentially Private Exponential Random Graphs
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 -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.Comment: minor edit
Noise Infusion as a Confidentiality Protection Measure for Graph-Based Statistics
We use the bipartite graph representation of longitudinally linked employer-employee data, and the associated projections onto the employer and employee nodes, respectively, to characterize the set of potential statistical summaries that the trusted custodian might produce. We consider noise infusion as the primary confidentiality protection method. We show that a relatively straightforward extension of the dynamic noise-infusion method used in the U.S. Census Bureau’s Quarterly Workforce Indicators can be adapted to provide the same confidentiality guarantees for the graph-based statistics: all inputs have been modified by a minimum percentage deviation (i.e., no actual respondent data are used) and, as the number of entities contributing to a particular statistic increases, the accuracy of that statistic approaches the unprotected value. Our method also ensures that the protected statistics will be identical in all releases based on the same inputs
Proceedings from the Synthetic LBD International Seminar
On May 9, 2017, we hosted a seminar to discuss the conditions necessary to im- plement the SynLBD approach with interested parties, with the goal of providing a straightforward toolkit to implement the same procedure on other data. The proceed- ings summarize the discussions during the workshop
- …