Model-based Differentially Private Data Synthesis
Fang Liu · arXiv (Cornell University) · 2016
We propose model-based based differential private data synthesis (modips) in the Bayesian framework for releasing individual-level surrogate data sets for the original with strong privacy guarantee. The modips technique integrates differential privacy (DP) -- a concept discussed largely in the theoretical computer science community -- into microdata synthesis in statistical disclosure limitation. The modips guarantees individual privacy protection at a given privacy budget without making assumptions about data intruder's behaviors and knowledge. The privacy budget can be used as tuning parameters in the trade-off between privacy protection and original information preservation in synthesized surrogate data. The uncertainty from the sanitization and synthetic process in the modips can be accounted for by releasing multiple synthetic data sets and by applying the proposed variance combination rule. We also characterize the conditions for the consistency of estimators based on released synthetic data. The modips method provides a viable alternative to the currently limited choice set of microdata synthesis approaches in statistical disclosure limitation.