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Title: Highly Efficient Optimal K-Anonymity for Biomedical Datasets

Citation Type: Conference Paper

Publication Year: 2012

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Abstract: K-anonymization is a wide-spread technique for the de-identification of biomedical datasets. To not render the data useless for further analysis it is often important to find an optimal solution to the k-anonymity problem, i.e., a transformation with minimum information loss. As performance is often a key requirement this paper describes an efficient implementation of a k-anonymization algorithm which is especially suitable for biomedical datasets. Although our basic implementation already offers excellent performance we present several further optimizations and show that these yield an additional speed-up of up to a factor of five, even for large datasets.

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Authors: Eckert, Claudia; Khun, Klaus A.; Prasser, Fabian; Kohlmayer, Florian; Kemper, Alfons

Conference Name: 25th IEEE International Symposium on Computer-Based Medical Systems

Publisher Location: Rome, Italy

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Topics: Other

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