Distributed anonymous data perturbation method for privacy-preserving data mining
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Feng LI, Jin MA, Jian-hua LI. Distributed anonymous data perturbation method for privacy-preserving data mining[J]. Journal of Zhejiang University Science A, 2009, 10(7): 952-963.
@article{title="Distributed anonymous data perturbation method for privacy-preserving data mining",
author="Feng LI, Jin MA, Jian-hua LI",
journal="Journal of Zhejiang University Science A",
volume="10",
number="7",
pages="952-963",
year="2009",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820320"
}
%0 Journal Article
%T Distributed anonymous data perturbation method for privacy-preserving data mining
%A Feng LI
%A Jin MA
%A Jian-hua LI
%J Journal of Zhejiang University SCIENCE A
%V 10
%N 7
%P 952-963
%@ 1673-565X
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820320
TY - JOUR
T1 - Distributed anonymous data perturbation method for privacy-preserving data mining
A1 - Feng LI
A1 - Jin MA
A1 - Jian-hua LI
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 7
SP - 952
EP - 963
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0820320
Abstract: Privacy is a critical requirement in distributed data mining. Cryptography-based secure multiparty computation is a main approach for privacy preserving. However, it shows poor performance in large scale distributed systems. Meanwhile, data perturbation techniques are comparatively efficient but are mainly used in centralized privacy-preserving data mining (PPDM). In this paper, we propose a light-weight anonymous data perturbation method for efficient privacy preserving in distributed data mining. We first define the privacy constraints for data perturbation based PPDM in a semi-honest distributed environment. Two protocols are proposed to address these constraints and protect data statistics and the randomization process against collusion attacks: the adaptive privacy-preserving summary protocol and the anonymous exchange protocol. Finally, a distributed data perturbation framework based on these protocols is proposed to realize distributed PPDM. Experiment results show that our approach achieves a high security level and is very efficient in a large scale distributed environment.
Full Text:
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CLC number: TP391.7
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2009-04-29
Cited: 3
Clicked: 8003
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