Distributed anonymous data perturbation method for privacy-preserving data mining
Distributed anonymous data perturbation method for privacy-preserving data mining
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DOI：
10.1631/jzus.a0820320
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发表时间：
2009-07
影响因子：
3.2
通讯作者：
Feng Li;Jin Ma;Jian-hua Li
中科院分区：
文献类型：
--
作者：
Feng Li;Jin Ma;Jian-hua Li
隐私是分布式数据挖掘的关键要求。基于密码学的安全多方计算是隐私保护的主要方法。然而，它在大规模分布式系统中表现出较差的性能。同时，数据扰动技术相对有效，但主要用于集中式隐私保护数据挖掘（PPDM）。在本文中，我们提出了一种轻量级匿名数据扰动方法，用于在分布式数据挖掘中有效保护隐私。我们首先在半诚实的分布式环境中定义基于数据扰动的 PPDM 的隐私约束。提出了两种协议来解决这些约束并保护数据统计和随机化过程免受共谋攻击：自适应隐私保护摘要协议和匿名交换协议。最后，提出了基于这些协议的分布式数据扰动框架来实现分布式PPDM。实验结果表明，我们的方法达到了很高的安全级别，并且在大规模分布式环境中非常有效。
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.