Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu


  • Josep Domingo-FerrerEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1499


Post-randomization method


The Post-RAndomization Method (PRAM) is a probabilistic, perturbative masking method for disclosure protection of categorical microdata. In the masked file, the scores on some categorical attributes for certain records in the original file are changed to a different score according to a prescribed probability mechanism, namely a Markov matrix. The Markov approach makes PRAM very general, because it encompasses noise addition, data suppression and data recoding.

Key Points

The PRAM matrix contains a row for each possible value of each attribute to be protected. This rules out using the method for continuous data. PRAM was invented by Gouweleeuw et~al. [1]. The information loss and disclosure risk in data masked with PRAM largely depend on the choice of the Markov matrix and are still (open) research topics [2].


Recommended Reading

  1. 1.
    Gouweleeuw JM, Kooiman P, Willenborg LCRJ, DeWolf P-P. Post randomisation for statistical disclosure control: theory and implementation, 1997. Statistics Netherlands. Voorburg: Research Paper No. 9731; 1997.Google Scholar
  2. 2.
    de Wolf P-P. Risk, utility and PRAM. In: Domingo-Ferrer J, Franconi L, editors. Privacy in statistical databases LNCS, vol. 4302. 2006. p. 189–204.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universitat Rovira i VirgiliTarragonaSpain

Section editors and affiliations

  • Elena Ferrari
    • 1
  1. 1.DiSTAUniv. of InsubriaVareseItaly