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.
Secure mining of association rules over horizontal partitioned data is addressed in REF .
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