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Attribute Compartmentation and Greedy UCC Discovery for High-Dimensional Data Anonymization by Podlesny, Nikolai J. ;Kayem, Anne V.D.M. ;Meinel, Christoph is a scholarly article available to read on EtoBox.

What is Attribute Compartmentation and Greedy UCC Discovery for High-Dimensional Data Anonymization about?

High-dimensional data is particularly useful for data analytics research. In the healthcare domain, for instance, high-dimensional data analytics has been used successfully for drug discovery. Yet, in order to adhere to privacy legislation, data analytics service providers must guarantee anonymity for data owners. In the context of high-dimensional data, ensuring privacy is challenging because increased data dimensionality must be matched by an exponential growth in the size of the data to avoid sparse datasets. Syntactically, anonymising sparse datasets with methods that rely of statistical significance, makes obtaining sound and reliable results, a challenge. As such, strong privacy is only achievable at the cost of high information loss, rendering the data unusable for data analytics. In this paper, we make two contributions to addressing this problem from both the privacy and information loss perspectives. First, we show that by identifying dependencies between attribute subsets we can eliminate privacy violating attributes from the anonymised dataset. Second, to minimise information loss, we employ a greedy search algorithm to determine and eliminate maximal partial unique att

Author

Podlesny, Nikolai J. ;Kayem, Anne V.D.M. ;Meinel, Christoph

Publisher

ACM Press

Published

2019

Language

EN