Abstract:
In this paper we study the impact of statistical disclosure limitation in the setting of parameter estimation for a finite population. Using a simulation experiment with microdata from the 2010 American Community Survey, we demonstrate a framework for applying risk-utility paradigms to microdata for a finite population, which incorporates a utility measure based on estimators with survey weights and risk measures based on record linkage techniques with composite variables. The simulation study shows a special caution on variance estimation for finite populations with the released data that are masked by statistical disclosure limitation. We also compare various disclosure limitation methods including a modified version of microaggregation that accommodates survey weights. The results confirm previous findings that a two-stage procedure, microaggregation with adding noise, is effective in terms of data utility and disclosure risk.
Keywords:
American Community Survey; Data utility; Disclosure risk; Microaggregation; Risk-utility paradigm; Replicate weights
tr183.pdf