Difference between revisions of "L-diversity"
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| − | An adversary can easily discover the values of sensitive attributes in a [[k-anonymity|k-anonymized]] dataset if there is little diversity in the sensitive attributes. l-diversity is a new definition of privacy that | + | An adversary can easily discover the values of sensitive attributes in a [[k-anonymity|k-anonymized]] dataset if there is little diversity in the sensitive attributes. l-diversity is a new definition of privacy that addresses this problem. A set of records in an equivalence class C is l-diverse if it contains at least l “well-represented” values for each sensitive attribute. |
== Related papers == | == Related papers == | ||
A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. l-diversity: Privacy beyond k-anonymity. In | A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. l-diversity: Privacy beyond k-anonymity. In | ||
Proc. 22nd Intnl. Conf. Data Engg. (ICDE), 2006. [http://www.cs.colostate.edu/~cs656/reading/ldiversity.pdf pdf] | Proc. 22nd Intnl. Conf. Data Engg. (ICDE), 2006. [http://www.cs.colostate.edu/~cs656/reading/ldiversity.pdf pdf] | ||
Latest revision as of 10:32, 6 November 2012
An adversary can easily discover the values of sensitive attributes in a k-anonymized dataset if there is little diversity in the sensitive attributes. l-diversity is a new definition of privacy that addresses this problem. A set of records in an equivalence class C is l-diverse if it contains at least l “well-represented” values for each sensitive attribute.
Related papers
A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. l-diversity: Privacy beyond k-anonymity. In Proc. 22nd Intnl. Conf. Data Engg. (ICDE), 2006. pdf
