Synthetic Data via Quantile Regression for Heavy-Tailed and Heteroskedastic Data

  • Michelle PistnerEmail author
  • Aleksandra Slavković
  • Lars Vilhuber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11126)


Privacy protection of confidential data is a fundamental problem faced by many government organizations and research centers. It is further complicated when data have complex structures or variables with highly skewed distributions. The statistical community addresses general privacy concerns by introducing different techniques that aim to decrease disclosure risk in released data while retaining their statistical properties. However, methods for complex data structures have received insufficient attention. We propose producing synthetic data via quantile regression to address privacy protection of heavy-tailed and heteroskedastic data. We address some shortcomings of the previously proposed use of quantile regression as a synthesis method and extend the work into cases where data have heavy tails or heteroskedastic errors. Using a simulation study and two applications, we show that there are settings where quantile regression performs as well as or better than other commonly used synthesis methods on the basis of maintaining good data utility while simultaneously decreasing disclosure risk.



The Synthetic LBD data were accessed through the Synthetic Data Server at Cornell University, which is funded through NSF Grant SES-1042181 and BCS-0941226 and a grant from the Alfred P. Sloan foundation. Access to the Synthetic LBD is described at Use of the Integrated Census Microdata Project at the University of Essex was facilitated by Gillian Raab.

Supplementary material


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Authors and Affiliations

  1. 1.Department of StatisticsThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Economics DepartmentCornell UniversityIthacaUSA

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