Adjusting survey weights when altering identifying design variables via synthetic data

Authors:
Robin Mitra;Jerome P. Reiter
Affiliations:
Duke University, Durham, NC;Duke University, Durham, NC
Venue:
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Year:
2006

Citing 0
Cited 1

Comparing Fully and Partially Synthetic Datasets for Statistical Disclosure Control in the German IAB Establishment Panel

Transactions on Data Privacy

Quantified Score

Hi-index	0.00

Visualization

Abstract

Statistical agencies alter values of identifiers to protect respondents' confidentiality. When these identifiers are survey design variables, leaving the original survey weights on the file can be a disclosure risk. Additionally, the original weights may not correspond to the altered values, which impacts the quality of design-based (weighted) inferences. In this paper, we discuss some strategies for altering survey weights when altering design variables. We do so in the context of simulating identifiers from probability distributions, i.e. partially synthetic data. Using simulation studies, we illustrate aspects of the quality of inferences based on the different strategies.