Distributed anonymous data perturbation method for privacy-preserving data mining

作者：Li, Feng^*; Ma, Jin; Li, Jian-hua

来源：Journal of Zhejiang University-Science A(Applied Physics & Engineering), 2009, 10(7): 952-963.

DOI：10.1631/jzus.A0820320

• Privacy-preserving data mining (PPDM)
• Distributed data mining
• Data perturbation

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.

• 出版日期2009-7
• 单位上海交通大学