STatistical Election to Partition Sequentially (STEPS) and Its Application in Differentially Private Release and Analysis of Youth Voter Registration Data

Voter data is important in political science research and applications such as improving youth voter turnout. Privacy protection is imperative in voter data since it often contains sensitive individual information. Differential privacy (DP) formalizes privacy in probabilistic terms and provides a robust concept for privacy protection. DIfferentially Private Data Synthesis (DIPS) techniques produce synthetic data in the DP setting. However, statistical efficiency of the synthetic data via DIPS can be low due to the potentially large amount of noise injected to satisfy DP, especially in high-dimensional data. We propose a new DIPS approach STatistical Election to Partition Sequentially (STEPS) that sequentially partitions data by attributes per their differentiability of the data variability. Additionally, we propose a metric SPECKS that effectively assesses the similarity of synthetic data to the actual data. The application of the STEPS procedure on the 2000-2012 Current Population Survey youth voter data suggests STEPS is easy to implement and better preserves the original information than some DIPS approaches including the Laplace mechanism on the full cross-tabulation of the data and the hierarchical histograms generated via random partitioning.