@ARTICLE{Kantarcioglu_04,
    AUTHOR      = {Murat Kantarcioglu and Chris Clifton},
    TITLE       = {{Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data}},
    JOURNAL     = {{IEEE Transactions on Knowledge and Data Engineering}},
    YEAR        = {2004},
    VOLUME      = {16},
    NUMBER      = {},
    PAGES       = {1026--1037},
    MONTH       = {September},
    NOTE        = {},
    KEYWORDS    = {},
    ISBN        = {},
    URL         = {http://www.cs.purdue.edu/homes/jsvaidya/pub-papers/kdd02.pdf},
    ABSTRACT    = {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.},
}