The large number of publicly available survey datasets of wide variety,
albeit useful, raise respondent-level privacy concerns. The synthetic data
approach to data privacy and confidentiality has been shown useful in terms of
privacy protection and utility preservation. This paper aims at illustrating
how synthetic data can facilitate the dissemination of highly sensitive
information about youth risk behavior by presenting a case study of synthetic
data for a sample of the Youth Risk Behavior Survey (YRBS). Given the
categorical nature of almost all variables in YRBS, the Dirichlet Process
mixture of products of multinomials (DPMPM) synthesizer is adopted to partially
synthesize the YRBS sample. Detailed evaluations of utility and disclosure
risks demonstrate that the generated synthetic data are able to significantly
reduce the disclosure risks compared to the confidential YRSB sample while
maintaining a high level of utility