Data mining can extract important knowledge from large data collections-but sometimes these collections are split among various parties. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. This paper addresses secure mining of association rules over horizontally partitioned data. The methods incorporate cryptographic techniques to minimize the information shared, while adding little overhead to the mining task.
Kantarcioglu and Clifton REF proposed two protocols to implement privacy-preserving mining of association rules over horizontally partitioned data.
Privacy-preserving distributed mining of association rules on horizontally partitioned data
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Computer Science