Synonyms
Re-identification risk; Attribute disclosure; Identity disclosure
Definition
In the context of statistical disclosure control, disclosure risk can be defined as the risk that a user or an intruder can use the protected dataset V′ to derive confidential information on an individual among those in the original dataset V. This approach to disclosure risk was formulated in Dalenius [1].
Key Points
Disclosure risk can be regarded from two different perspectives, according to Paass [2]:
1. Attribute disclosure. Attribute disclosure takes place when an attribute of an individual can be determined more accurately with access to the released statistic than it is possible without access to that statistic.
2. Identity disclosure. Identity disclosure takes place when a record in the protected dataset can be linked with a respondent's identity. Two main approaches are usually employed for measuring identity disclosure risk: uniqueness and re-identification.
2.1. UniquenessRoughly...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Dalenius T. Towards a methodology for statistical disclosure control. Statistisk Tidskrift, 5:429–444, 1977.
Paass G. Disclosure risk and disclosure avoidance for microdata. J. Bus. Econ. Stat., 6:487–500, 1985.
Spruill N.L. The confidentiality and analytic usefulness of masked business microdata. In Proc. Section on Survey Research Methods, American Statistical Association, Alexandria, VA. 1983, pp. 602–607.
Winkler W.E. Re-identification methods for masked microdata. In Privacy in Statistical Databases, J. Domingo-Ferrer and V. Torra (eds.). vol. 3050, Springer, Berlin Heidelberg, 2004, pp. 216–230.LNCS,
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this entry
Cite this entry
Domingo-Ferrer, J. (2009). Disclosure Risk. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1506
Download citation
DOI: https://doi.org/10.1007/978-0-387-39940-9_1506
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-35544-3
Online ISBN: 978-0-387-39940-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering