Statistical disclosure control methods through a risk-utility framework

Authors:
Natalie Shlomo;Caroline Young
Affiliations:
Southampton Statistical Sciences Research Institute, University of Southampton, UK, and the Department of Statistics, Hebrew University, Mt. Scopus, Jerusalem, Israel;School of Social Sciences, University of Southampton, Southampton, UK and the Office, for National Statistics, Fareham, UK
Venue:
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Year:
2006

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Abstract

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.