2008 IEEE 24th International Conference on Data Engineering (2008)
Apr. 7, 2008 to Apr. 12, 2008
Gabriel Ghinita , Department of Computer Science, National University of Singapore, Computing 1, Singapore 117590. firstname.lastname@example.org
Yufei Tao , Department of Computer Science and Engineering, Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong SAR, China. email@example.com
Panos Kalnis , Department of Computer Science, National University of Singapore, Computing 1, Singapore 117590. firstname.lastname@example.org
Existing research on privacy-preserving data publishing focuses on relational data: in this context, the objective is to enforce privacy-preserving paradigms, such as k-anonymity and l-diversity, while minimizing the information loss incurred in the anonymizing 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-dimensional 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.
Y. Tao, G. Ghinita and P. Kalnis, "On the Anonymization of Sparse High-Dimensional Data," 2008 IEEE 24th International Conference on Data Engineering(ICDE), Cancun, Mexico, 2008, pp. 715-724.