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The large number of publicly available survey datasets of wide variety,albeit useful, raise respondent-level privacy concerns. The synthetic dataapproach to data privacy and confidentiality has been shown useful in terms ofprivacy protection and utility preservation. This paper aims at illustratinghow synthetic data can facilitate the dissemination of highly sensitiveinformation about youth risk behavior by presenting a case study of syntheticdata for a sample of the Youth Risk Behavior Survey (YRBS). Given thecategorical nature of almost all variables in YRBS, the Dirichlet Processmixture of products of multinomials (DPMPM) synthesizer is adopted to partiallysynthesize the YRBS sample. Detailed evaluations of utility and disclosurerisks demonstrate that the generated synthetic data are able to significantlyreduce the disclosure risks compared to the confidential YRSB sample whilemaintaining a high level of utility.

Privacy Protection for Youth Risk Behavior Using Bayesian Data Synthesis: A Case Study to the YRBS