Quantitative Association Rules Mining Methods with Privacy-preserving

Authors:
CHEN Zi-Yang;LIU Guo-Hua
Affiliations:
Yanshan University, Qinhuangdao, China;Yanshan University, Qinhuangdao, China
Venue:
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
Year:
2005

Citing 0
Cited 3

Correlated pattern mining in quantitative databases

ACM Transactions on Database Systems (TODS)
An information-theoretic approach to quantitative association rule mining

Knowledge and Information Systems
Target-based privacy preserving association rule mining

Proceedings of the 2011 ACM Symposium on Applied Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Considering the different size of quantitative attribute values and categorical attribute values in databases, we present two quantitative association rules mining methods with privacy-preserving respectively, one bases on Boolean association rules, which is suitable for the smaller size of quantitative attribute values and categorical attribute values in databases; the other one bases on partially transforming measures, which is suitable for the larger ones. To each approach, the privacy and accuracy are analyzed, and the correctness and feasibility are proven by experiments.