Uncovering the structures of privacy research using bibliometric network analysis and topic modelling

Friso van Dijk, Joost Gadellaa, Chaïm van Toledo, Marco Spruit, Sjaak Brinkkemper, Matthieu Brinkhuis

Research output: Contribution to journalArticleAcademicpeer-review


This paper aims that privacy research is divided in distinct communities and rarely considered as a singular field, harming its disciplinary identity. The authors collected 119.810 publications and over 3 million references to perform a bibliometric domain analysis as a quantitative approach to uncover the structures within the privacy research field.

The bibliometric domain analysis consists of a combined directed network and topic model of published privacy research. The network contains 83,159 publications and 462,633 internal references. A Latent Dirichlet allocation (LDA) topic model from the same dataset offers an additional lens on structure by classifying each publication on 36 topics with the network data. The combined outcomes of these methods are used to investigate the structural position and topical make-up of the privacy research communities.

The authors identified the research communities as well as categorised their structural positioning. Four communities form the core of privacy research: individual privacy and law, cloud computing, location data and privacy-preserving data publishing. The latter is a macro-community of data mining, anonymity metrics and differential privacy. Surrounding the core are applied communities. Further removed are communities with little influence, most notably the medical communities that make up 14.4% of the network. The topic model shows system design as a potentially latent community. Noteworthy is the absence of a centralised body of knowledge on organisational privacy management.

This is the first in-depth, quantitative mapping study of all privacy research.
Original languageEnglish
JournalOrganizational Cybersecurity Journal: Practice, Process and People
Publication statusPublished - 14 Feb 2023


  • Privacy
  • Bibliometric
  • Mapping
  • Network
  • Topic model


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