Abstract
Statistical agencies assess the risk of disclosure before releasing data. Unacceptably high disclosure risk will prevent a statistical agency from disseminating the data. The application of statistical disclosure control (SDC) methods aims to provide sufficient protection and make the data release possible. The disclosure risk of tabular data is typically quantified at the level of table cells. However, the evaluation of disclosure risk can require the assessment of the table as a whole, for example in the case of online flexible table generators. In this paper we use information theory to develop a disclosure risk measure for population-based frequency tables. The proposed disclosure risk measure quantifies the risk of attribute disclosure before and after an SDC method is applied. The new measure is compared to alternative disclosure risk measures developed at the Office for National Statistics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Antal, L., Shlomo, N., Elliot, M.: Measuring Disclosure Risk and Information Loss in Population Based Frequency Tables, http://www.ccsr.ac.uk/publications/Measuring_Disclosure_Risk
Cover, T.M., Thomas, J.A.: Elements of Information Theory, 2nd edn. Wiley, Hoboken (2006)
Domingo-Ferrer, J., Oganian, A., Torra, V.: Information-Theoretic Disclosure Risk Measures in Statistical Disclosure Control of Tabular Data. In: Proceedings of the 14th International Conference on Scientific and Statistical Database Management, Washington, pp. 227–231 (2002)
Duncan, G., Keller-McNulty, S., Stokes, S.: Disclosure Risk vs. Data Utility: the R-U Confidentiality Map. Technical Report LA-UR-01-6428, Statistical Sciences Group. Los Alamos National Laboratory, Los Alamos, N.M (2001)
Oganian, A., Domingo-Ferrer, J.: A Posteriori Disclosure Risk Measure for Tabular Data Based on Conditional Entropy. SORT-Statistics and Operations Research Transactions 27, 175–190 (2003)
Oganian, A., Domingo-Ferrer, J., Torra, V.: Internal Intrusion Scenarios in Inference Control of Tabular Databases. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems (2004)
Shlomo, N.: Statistical Disclosure Control Methods for Census Frequency Tables. International Statistical Review 75, 199–217 (2007)
Willenborg, L., de Waal, T.: Elements of Statistical Disclosure Control. Lecture Notes in Statistics. Springer (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Antal, L., Shlomo, N., Elliot, M. (2014). Measuring Disclosure Risk with Entropy in Population Based Frequency Tables. In: Domingo-Ferrer, J. (eds) Privacy in Statistical Databases. PSD 2014. Lecture Notes in Computer Science, vol 8744. Springer, Cham. https://doi.org/10.1007/978-3-319-11257-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-11257-2_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11256-5
Online ISBN: 978-3-319-11257-2
eBook Packages: Computer ScienceComputer Science (R0)