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A General Framework and Metrics for Longitudinal Data Anonymization

  • Nicolas RuizEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11126)

Abstract

The bulk of methods in statistical disclosure control primarily deal with individual data from a cross-sectional perspective, i.e. data where individuals are observed at one single point in time. However, nowadays longitudinal data, i.e. individuals observed over multiple periods, are increasingly collected. Such data enhance undoubtedly the possibility of statistical analysis compared to cross-sectional data, but also come with some additional layers of information that have to remain practically useful in a privacy-preserving way. Building on the recently proposed permutation paradigm as an overarching approach to data anonymization, this paper establishes a general framework for the formulation of longitudinal data anonymization and proposes some universal metrics for the assessment of disclosure risk and information loss. We illustrate the application of these new tools using an empirical example.

Keywords

Statistical disclosure control Longitudinal data Permutation paradigm 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Mathematics, CYBERCAT-Center for Cybersecurity Research of Catalonia UNESCO Chair in Data PrivacyUniversitat Rovira i VirgiliTarragonaSpain

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