Privacy-Preserving Distributed Data Fusion Based on Attribute Protection
Xin Su, Kuan Gang Fan, Wenbo Shi · IEEE Transactions on Industrial Informatics · 2019
Privacy-preserving distributed data fusion is a pretreatment process in data mining involving security models. In this paper, we present a method of implementing multiparty data fusion, wherein redundant attributes of a same set of individuals are stored by multiple parties. In particular, the merged data does not suffer from background attacks or other reasoning attacks, and individual attributes are not leaked. To achieve this, we present three algorithms that satisfy K-anonymous and differential privacy. Experimental results on real datasets suggest that the proposed algorithm can effectively preserve information in data mining tasks.