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How Protective Are Synthetic Data?

  • John M. Abowd
  • Lars Vilhuber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5262)

Abstract

This short paper provides a synthesis of the statistical disclosure limitation and computer science data privacy approaches to measuring the confidentiality protections provided by fully synthetic data. Since all elements of the data records in the release file derived from fully synthetic data are sampled from an appropriate probability distribution, they do not represent “real data,” but there is still a disclosure risk. In SDL this risk is summarized by the inferential disclosure probability. In privacy-protected database queries, this risk is measured by the differential privacy ratio. The two are closely related. This result (not new) is demonstrated and examples are provided from recent work.

Keywords

Conditional Distribution Release Data Synthetic Data Laplace Distribution Differential Privacy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • John M. Abowd
    • 1
  • Lars Vilhuber
    • 1
  1. 1.School of Industrial and Labor RelationsCornell University 

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