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Differential Privacy (DP) formalizes privacy in mathematical terms andprovides a robust concept for privacy protection. DIfferentially Private DataSynthesis (DIPS) techniques produce and release synthetic individual-level datain the DP framework. One key challenge to developing DIPS methods ispreservation of the statistical utility of synthetic data, especially inhigh-dimensional settings. We propose a new DIPS approach, STatistical Electionto Partition Sequentially (STEPS) that partitions data by attributes accordingto their importance ranks according to either a practical or statisticalimportance measure. STEPS aims to achieve better original informationpreservation for the attributes with higher importance ranks and produce thusmore useful synthetic data overall. We present an algorithm to implement theSTEPS procedure and employ the privacy budget composability to ensure theoverall privacy cost is controlled at the pre-specified value. We apply theSTEPS procedure to both simulated data and the 2000-2012 Current PopulationSurvey youth voter data. The results suggest STEPS can better preserve thepopulation-level information and the original information for some analysescompared to PrivBayes, a modified Uniform histogram approach, and the flatLaplace sanitizer.

Differentially Private Data Release via Statistical Election to Partition Sequentially