Statistical Disclosure Control Methods Through a Risk-Utility Framework

This paper discusses a disclosure risk – data utility framework for assessing statistical disclosure control (SDC) methods on statistical data. Disclosure risk is defined in terms of identifying individuals in small cells in the data which then leads to attribute disclosure of other sensitive variables. Information Loss measures are defined for assessing the impact of the SDC method on the utility of the data and its effects when carrying out standard statistical analysis tools. The quantitative disclosure risk and information loss measures can be plotted onto an R-U confidentiality map for determining optimal SDC methods. A user-friendly software application has been developed and implemented at the UK Office for National Statistics (ONS) to enable data suppliers to compare original and disclosure controlled statistical data and to make informed decisions on best methods for protecting their statistical data.

Authors and Affiliations

1. Southampton Statistical Sciences Research Institute, University of Southampton, UK, and the Department of Statistics, Hebrew University, Mt. Scopus, Jerusalem, Israel

   Natalie Shlomo

2. School of Social Sciences, University of Southampton, Southampton, UK and the Office, for National Statistics, Segensworth Road, Fareham, UK

   Caroline Young

Editors and Affiliations

1. Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, UNESCO Chair in Data Privacy, Av. Països Catalans 26, E-43007, Tarragona, Catalonia

   Josep Domingo-Ferrer

2. Istat, Servizio Progettazione e Supporto Metodologico, nei Processi di Produzione Statistica, Via Cesare Balbo 16, 00184, Roma, Italy

   Luisa Franconi

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