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Abstract:
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When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals who contribute to the data. Data synthesis (DS) is a statistical disclosure limitation technique for releasing synthetic data sets with pseudo individual records. Traditional DS techniques often rely on strong assumptions on a data intruder's behaviors and background knowledge to assess disclosure risk. Differential privacy (DP) formulates a theoretical approach for strong and robust privacy guarantee in data release without having to model intruders' behaviors. Efforts have been made aiming to incorporate the DP concept in the DS process. In this paper, we examine current DIfferentially Private Data Synthesis (DIPS) techniques, compare the techniques conceptually, and evaluate the statistical utility and inferential properties of the synthetic data via each DIPS technique through extensive simulation studies. Our work sheds light on the practical feasibility and utility of the various DIPS approaches, and suggests future research directions for DIPS.
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