An improved EMASK algorithm for privacy-preserving frequent pattern mining

Authors:
Congfu Xu;Jinlong Wang;Hongwei Dan;Yunhe Pan
Affiliations:
Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China;Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China;Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China;Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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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 BV-EMASK reduces the execution time significantly when comparing with EMASK.