Wavelet-based data perturbation for simultaneous privacy-preserving and statistics-preserving

Lian Liu, Jie Wang, Jun Zhang

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

21 Scopus citations

Abstract

With the rapid development of data mining technologies, preserving privacy in certain data becomes a challenge to data mining applications in many fields, especially in medical, financial and homeland security fields. We present a privacy-preserving strategy based on wavelet perturbation to keep the data privacy and data statistical properties and data mining utilities at the same time. Our mathematical analyses and experimental results show that this method can keep the distance before and after perturbation and it can preserve the basic statistical properties of the original data while maximizing the data utilities. Through experiments on real-life datasets, we conclude that this method is a promising privacy-preserving and statistics-preserving technique.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
Pages27-35
Number of pages9
DOIs
StatePublished - 2008
EventIEEE International Conference on Data Mining Workshops, ICDM Workshops 2008 - Pisa, Italy
Duration: Dec 15 2008Dec 19 2008

Publication series

NameProceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008

Conference

ConferenceIEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
Country/TerritoryItaly
CityPisa
Period12/15/0812/19/08

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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