Comparative Study of Differentially Private Data Synthesis Methods
When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals in the data set. Data synthesis is a statistical disclosure limitation technique for releasing synthetic data sets with pseudo individual records. Traditional data synthesis techniques often rely on strong assumptions of a data intruder’s behaviors and background knowledge to assess disclosure risk. Differential privacy (DP) formulates a theoretical approach for a 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 data synthesis process. Here, we examine current DIfferentially Private Data Synthesis (DIPS) techniques for releasing individual-level surrogate data for the original data, 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.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE; National Science Foundation (NSF)
- Grant/Contract Number:
- 89233218CNA000001; DGE-1313583; 1546373; 1717417
- OSTI ID:
- 1822720
- Report Number(s):
- LA-UR-18-31132
- Journal Information:
- Statistical Science, Vol. 35, Issue 2; ISSN 0883-4237
- Publisher:
- Institute of Mathematical StatisticsCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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