Multivariate equi-width data swapping for private data publication
Date
2010
Authors
Li, Y.
Shen, H.
Editors
Zaki, M.J.
Yu, J.X.
Ravindran, B.
Pudi, V.
Yu, J.X.
Ravindran, B.
Pudi, V.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Lecture Notes in Computer Science, 2010, 6118/2010: 208-215
Statement of Responsibility
Yidong Li and Hong Shen
Conference Name
Pacific-Asia Conference on Knowledge Discovery and Data Mining (14th : 2010 : Hyderabad, India)
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.
School/Discipline
Dissertation Note
Provenance
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
Access Status
Rights
© Springer-Verlag Berlin Heidelberg 2010