Controlling Privacy Loss in Survey Sampling (Working Paper)
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Social science and economics research is often based on data collected in surveys. Due to time and budgetary constraints, this data is often collected using complex sampling schemes designed to increase accuracy while reducing the costs of data collection. A commonly held belief is that the sampling process affords the data subjects some additional privacy. This intuition has been formalized in the differential privacy literature for simple random sampling: a differentially private mechanism run on a simple random subsample of a population provides higher privacy guarantees than when run on the entire population. In this work we initiate the study of the privacy implications of more complicated sampling schemes including cluster sampling and stratified sampling. We find that not only do these schemes often not amplify privacy, but that they can result in privacy degradation.
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