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2005
Springer

An Improved EMASK Algorithm for Privacy-Preserving Frequent Pattern Mining

10 years 9 months ago
An Improved EMASK Algorithm for Privacy-Preserving Frequent Pattern Mining
Abstract. As a novel research direction, privacy-preserving data mining (PPDM) has received a great deal of attentions from more and more researchers, and a large number of PPDM algorithms use randomization distortion techniques to mask the data for preserving the privacy of sensitive data. In reality, for PPDM in the data sets, which consist of terabytes or even petabytes of data, efficiency is a paramount important consideration in addition to the requirements of privacy and accuracy. Recently, EMASK, an efficient privacy-preserving frequent pattern mining algorithm, was proposed. Motivated by EMASK, in this paper, we improve on it, and present an improved algorithm BV-EMASK to furthermore enhance efficiency. Performance evaluation shows that BVEMASK reduces the execution time significantly when comparing with EMASK.
Congfu Xu, Jinlong Wang, Hongwei Dan, Yunhe Pan
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where CIS
Authors Congfu Xu, Jinlong Wang, Hongwei Dan, Yunhe Pan
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