Hierarchical infrastructure for large-scale distributed privacy-preserving data mining

Authors:
Jinlong Wang;Congfu Xu;Huifeng Shen;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:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Year:
2005

Citing 5
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Abstract

Data Mining is often required to be performed among a number of groups of sites, where the precondition is that no privacy of any site should be leaked out to other sites. In this paper, a hierarchical infrastructure is proposed for large-scale distributed Privacy Preserving Data Mining (PPDM) utilizing a synergy between P2P and Grid. The proposed architecture is characterized with (1) its ability for preserving the privacy in data mining; (2) its ability for decentralized control; (3) its dynamic and scalable ability; (4) its global asynchrony and local communication ability. An algorithm is described to show how to process large-scale distributed PPDM based on the infrastructure. The remarks in the end show the effectiveness and advantages of the proposed infrastructure for large-scale distributed PPDM.