Issue No. 09 - September (2004 vol. 16)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2004.45
Chris Clifton , IEEE
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.
Data mining, security, privacy.
Murat Kantarcioglu, Chris Clifton, "Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data", IEEE Transactions on Knowledge & Data Engineering, vol. 16, no. , pp. 1026-1037, September 2004, doi:10.1109/TKDE.2004.45