Publications [#258523] of Jerome P. Reiter

Papers Published

1. Hu, J; Reiter, JP; Wang, Q, Disclosure risk evaluation for fully synthetic categorical data, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8744 (January, 2014), pp. 185-199, ISSN 0302-9743, ISBN 9783319112565 [doi]
   (last updated on 2026/01/21)

   Abstract:
   © Springer International Publishing Switzerland 2014. We present an approach for evaluating disclosure risks for fully synthetic categorical data. The basic idea is to compute probability distributions of unknown confidential data values given the synthetic data and assumptions about intruder knowledge. We use a “worst-case” scenario of an intruder knowing all but one of the records in the confidential data. To create the synthetic data, we use a Dirichlet process mixture of products of multinomial distributions, which is a Bayesian version of a latent class model. In addition to generating synthetic data with high utility, the likelihood function admits simple and convenient approximations to the disclosure risk probabilities via importance sampling. We illustrate the disclosure risk computations by synthesizing a subset of data from the American Community Survey.