Abstract
Releasing, publishing or transferring microdata is restricted by the necessity to protect the privacy of data owners. K-anonymity is one of the most widespread concepts for anonymizing microdata but it does not explicitly cover NULL values frequently found in microdata. We study the problem of NULL values (missing values, non-applicable attributes, etc.) for anonymization in detail, present a set of new definitions for k-anonymity explicitly considering NULL and analyze which definition protects from which attacks. We show that an adequate treatment of missing values in microdata can be easily achieved by an extension of generalization algorithms and show that NULL aware generalization algorithms have less information loss than standard algorithms.
The work reported here was supported by the Austrian Ministry of Science and Research within the project BBMRI.AT and the Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF) within the project ANON.
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Ciglic, M., Eder, J., Koncilia, C. (2014). k-Anonymity of Microdata with NULL Values. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds) Database and Expert Systems Applications. DEXA 2014. Lecture Notes in Computer Science, vol 8644. Springer, Cham. https://doi.org/10.1007/978-3-319-10073-9_27
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DOI: https://doi.org/10.1007/978-3-319-10073-9_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10072-2
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