LUGS: A Scalable Non-parametric Data Synthesizer for Privacy-Preserving Health Data Publiction

This paper introduces a non-parametric data synthesizing algorithm to generate privacysafe “realistic but not real” synthetic health data. The proposed algorithm synthesizes artificial records while preserving the statistical characteristics of the original data to the extent possible. The risk from “database linking attack” is quantified by an l-diversified data generation process. Moreover its algorithmic performance is optimized using Locality-Sensitive Hashing and parallel computation techniques to yield a linear-time algorithm that is suitable for Big Health data applications. We synthesize a public Medicare claim dataset using the proposed algorithm, and demonstrate multiple data mining applications and statistical analyses using the data. The synthetic dataset delivers results that are substantially identical to those obtained from the original dataset, without revealing the actual records.