In many privacy preserving applications, specific variables are required to be disturbed simultaneously in order to guarantee correlations among them. Multivariate Equi-Depth Swapping (MEDS) is a natural solution in such cases, since it provides uniform privacy protection for each data tuple. However, this approach performs ineffectively not only in computational complexity (basically O(n3) for n data tuples), but in data utility for distance-based data analysis. This paper discusses the utilisation of Multivariate Equi-Width Swapping (MEWS) to enhance the utility preservation for such cases. With extensive theoretical analysis and experimental results, we show that, MEWS can achieve a similar performance in privacy preservation to that of MEDS and has only O(n) computational complexity. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Li, Y., & Shen, H. (2010). Multivariate equi-width data swapping for private data publication. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6118 LNAI, pp. 208–215). https://doi.org/10.1007/978-3-642-13657-3_24
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