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Correcting Finite Sampling Issues in Entropy l-diversity

  • Sebastian Stammler
  • Stefan Katzenbeisser
  • Kay Hamacher
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9867)

Abstract

In statistical disclosure control (SDC) anonymized versions of a database table are obtained via generalization and suppression to reduce de-anonymization attacks, ideally with minimal utility loss. This amounts to an optimization problem in which a measure of remaining diversity needs to be improved. The feasible solutions are those that fulfill some privacy criteria, e.g., the entropy l-diversity. In the statistics it is known that the naive computation of an entropy via the Shannon formula systematically underestimates the (real) entropy and thus influences the resulting equivalence classes. In this contribution we implement an asymptotically unbiased estimator for the Shannon entropy and apply it to three test databases. Our results show previously performed systematic miscalculations; we show that by an unbiased estimator one can increase the utility of the data without compromising privacy.

Keywords

Anonymity l-diversity Finite sampling Statistics Information theory 

Notes

Acknowledgements

The research reported in this paper has been supported by the German Federal Ministry of Education and Research (BMBF) [and by the Hessian Ministry of Science and the Arts] within CRISP (www.crisp-da.de).

We also thank the ARX-Team for their helpful support in using the API and understanding some inner workings of the framework.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sebastian Stammler
    • 1
  • Stefan Katzenbeisser
    • 1
  • Kay Hamacher
    • 1
  1. 1.Technische Universität DarmstadtDarmstadtGermany

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