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.
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Project supported by the National Natural Science Foundation of China (Nos. 60772098 and 60672068), and the New Century Excellent Talents in University of China (No. NCET-06-0393)
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Li, F., Ma, J. & Li, Jh. Distributed anonymous data perturbation method for privacy-preserving data mining. J. Zhejiang Univ. Sci. A 10, 952–963 (2009). https://doi.org/10.1631/jzus.A0820320
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DOI: https://doi.org/10.1631/jzus.A0820320