Please use this identifier to cite or link to this item:
|Title:||On the anonymization of sparse high-dimensional data|
|Citation:||Ghinita, G., Tao, Y., Kalnis, P. (2008). On the anonymization of sparse high-dimensional data. Proceedings - International Conference on Data Engineering : 715-724. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDE.2008.4497480|
|Abstract:||Existing research on privacy-preserving data publishing focuses on relational data: in this context, the objective is to enforce privacy-preserving paradigms, such as kanonymity and ℓ-diversity, while minimizing the information loss incurred in the anonyrnizing process (i.e. maximize data utility). However, existing techniques adopt an indexing- or clustering-based approach, and work well for fixed-schema data, with low dimensionality. Nevertheless, certain applications require privacy-preserving publishing of transaction data (or basket data), which involves hundreds or even thousands of dimensions, rendering existing methods unusable. We propose a novel anonymization method for sparse high-dlmensional data. We employ a particular representation that captures the correlation in the underlying data, and facilitates the formation of anonymized groups with low information loss. We propose an efficient anonymization algorithm based on this representation. We show experimentally, using real-life datasets, that our method clearly outperforms existing state-of-the-art in terms of both data utility and computational overhead. © 2008 IEEE.|
|Source Title:||Proceedings - International Conference on Data Engineering|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 12, 2019
WEB OF SCIENCETM
checked on Jan 2, 2019
checked on Jan 13, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.