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Type: Conference paper
Title: Multivariate equi-width data swapping for private data publication
Author: Li, Y.
Shen, H.
Citation: Lecture Notes in Computer Science, 2010, 6118/2010: 208-215
Publisher: Springer-Verlag Berlin
Publisher Place: Heidelberger Platz 3 Berlin Germany D-14197
Issue Date: 2010
Series/Report no.: Lecture Notes in Artificial Intelligence
ISBN: 3642136567
ISSN: 0302-9743
Conference Name: Pacific-Asia Conference on Knowledge Discovery and Data Mining (14th : 2010 : Hyderabad, India)
Editor: Zaki, M.J.
Yu, J.X.
Ravindran, B.
Pudi, V.
Statement of
Yidong Li and Hong Shen
Abstract: 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(n 3) 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.
Keywords: Private data publication
data swapping
equi-width partitioning
multivariate data perturbation
Description: Also published in: Advances in knowledge discovery and data mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010: Proceedings, Part I / Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran and Vikram Pudi (eds.), pp. 208-215
Rights: © Springer-Verlag Berlin Heidelberg 2010
DOI: 10.1007/978-3-642-13657-3_24
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Computer Science publications

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