PPDAM: Privacy-Preserving Distributed Association-Rule-Mining Algorithm

PPDAM: Privacy-Preserving Distributed Association-Rule-Mining Algorithm

Mafruz Zaman Ashrafi, David Taniar, Kate Smith
Copyright: © 2005 |Pages: 21
DOI: 10.4018/jiit.2005010104
OnDemand:
(Individual Articles)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

Data mining is a process that analyzes voluminous digital data in order to discover hidden but useful patterns from digital data. However, the discovering of such hidden patterns has statistical meaning and may often disclose some sensitive information. As a result, privacy becomes one of the prime concerns in the data-mining research community. Since distributed association mining discovers association rules by combining local models from various distributed sites, breaching data privacy happens more often than it does in centralized environments. In this work, we present a methodology that generates association rules without revealing confidential inputs such as statistical properties of individual sites, and yet retains a high level of accuracy in the resultant rules. One of the important outcomes of the proposed technique is that it reduces the overall communication costs. Performance evaluation of our proposed method shows that it reduces the communication cost significantly when we compare it with other well-known, distributed association-rule-mining algorithms. Nevertheless, the global rule model generated by the proposed method is based on the exact global support of each item set and hence diminishes inconsistency, which indeed occurs when global models are generated from partial support count of an item set.

Complete Article List

Search this Journal:
Reset
Volume 20: 1 Issue (2024)
Volume 19: 1 Issue (2023)
Volume 18: 4 Issues (2022): 3 Released, 1 Forthcoming
Volume 17: 4 Issues (2021)
Volume 16: 4 Issues (2020)
Volume 15: 4 Issues (2019)
Volume 14: 4 Issues (2018)
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing