The goal of the project is to enhance synthetic data techniques for practical applications. Specifically, the aims will develop novel methods to improve disclosure risk assessment, quality check verification, and population generalizability in the adjustment of complex survey design and weights. Yajuan Si will offer expertise on Bayesian and survey statistics and experiences in survey operation and weighting adjustments. She will extend the methodology development on synthetic population generation and data integration to confidentiality protection. She will apply the proposed methods to enhance practical confidentiality protection through her close collaborations with colleagues from the Panel Study of Income Dynamics (PSID) and the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan.