Multivariate equi-width data swapping for private data publication

Yidong Li, Hong Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
Number of pages8
EditionPART 1
Publication statusPublished - 2010
Externally publishedYes
Event14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 - Hyderabad, India
Duration: 21 Jun 201024 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6118 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010


  • Data swapping
  • Equi-width partitioning
  • Multivariate data perturbation
  • Private data publication


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