The Quasi-Multinomial Synthesizer for Categorical Data

  • Jingchen HuEmail author
  • Nobuaki Hoshino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11126)


We present a new synthesizer for categorical data based on the Quasi-Multinomial distribution. Characteristics of the Quasi-Multinomial distribution provide a tuning parameter, which allows a Quasi-Multinomial synthesizer to control the balance of the utility and the disclosure risks of synthetic data. We develop a Quasi-Multinomial synthesizer based on a popular categorical data synthesizer, the Dirichlet process mixtures of products of multinomial distributions. The general sampling methods and algorithm of the Quasi-Multinomial synthesizer are developed and presented. We illustrate its balance of the utility and the disclosure risks by synthesizing a sample from the American Community Survey.


Bayesian Dirichlet process Microdata Quasi-Multinomial Synthetic 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Vassar CollegePoughkeepsieUSA
  2. 2.Kanazawa UniversityKanazawaJapan

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