Abstract
Statistical disclosure control (SDC) is a set of methods that are used to reduce the risk of disclosing information on individuals, businesses or other organisations. The focus of this paper is on sensitivity rules, which deal with how to define whether a cell in tabular data has the risk of disclosing information or not.
The current popular sensitivity rules include the dominance rule and the P% rule. There is a weakness with these rules and a new rule - the interval rule is presented. The main argument for this new rule is that the rule should only be based on the information that the intruder knows, not on the information that the statistical institution knows.
Based on simulated data, the P% rule tends to classify a dataset to be “sensitive” when it contains only one observation with a very large value. In this respect, and the dominance rule and the P% rule share a lot in common. Meanwhile the interval rule tends to classify a dataset to be “sensitive” when it contains two observations with large values.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Castro, J.: Statistical disclosure control in tabular data. In: Privacy and Anonymity in Information Management Systems: New Techniques for New Practical Problem, pp. 113–131. Springer (2010)
Doyle, P., Lane, J.I., Theeuwes, J.J.M., Zayatz, L.V. (eds.): Confidentiality, disclosure, and data access: Theory and practical applications for statistical agencies, p. 1. Elsevier, Amsterdam (2001)
Hundepool, A., Domingo-Ferrer, J., Franconi, L., Giessing, S., Schulte Nordholt, E., Spicer, K., de Wolf, P.P.: Statistical Disclosure Control. Wiley (2012)
Loeve, J.A.: Notes on sensitivity measures and protection levels. Project number: TMO-102966, Statistics Netherlands (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
Bring, J., Wang, Q. (2014). Comparison of Different Sensitivity Rules for Tabular Data and Presenting a New Rule – The Interval Rule . 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_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-11257-2_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11256-5
Online ISBN: 978-3-319-11257-2
eBook Packages: Computer ScienceComputer Science (R0)