Online Program Home
My Program

Abstract Details

Activity Number: 591 - Synthetic Data and Data Disclosure
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: Government Statistics Section
Abstract #328363 Presentation 1 Presentation 2
Title: Differentially Private Multiple Synthesis via an Adaptive Multiplicative Weighting Algorithm
Author(s): Evercita Eugenio* and Fang Liu
Companies: University of Notre Dame and University of Notre Dame
Keywords: differential privacy; multiplicative weights; data synthesis; statistical disclosure limitation
Abstract:

An increased effort for transparency and accountability has led government agencies, businesses, and social media platforms to publicly release more of their data. The simple anonymized methods that have long been employed do not provide a high level of privacy protection, as data intruders can combine their knowledge with public information to identify subjects. The recently developed differentially private data synthesis (DIPS) methods are built upon the concept of differential privacy that provides a strong mathematical privacy guarantee while aiming to maintain the analytical validity and utility of the released sanitized data. We will present an adaptive mechanism that uses multiplicative weighting and variable selection techniques to account for the important variable relationships to generate DIPS data. Simulation and case study results will be presented along with comparisons to some existing DIPS methods.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program