@INPROCEEDINGS{Schuster_04b,
    AUTHOR      = {Assaf Schuster and Ran Wolff and Bobi Gilburd},
    TITLE       = {{Privacy-Preserving Association Rule Mining in Large-Scale Distributed Systems }},
    BOOKTITLE   = {{4th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid’04)}},
    YEAR        = {2004},
    EDITOR      = {},
    PAGES       = {},
    PUBLISHER   = {},
    VOLUME      = {},
    NUMBER      = {},
    SERIES      = {},
    ADDRESS     = {Illinois , USA},
    MONTH       = {April},
    NOTE        = {},
    KEYWORDS    = {},
    ISBN        = {},
    URL         = {http://www.cs.technion.ac.il/~assaf/publications/P4ARM_CCGrid.pdf},
    ABSTRACT    = {{Data privacy is a major concern that threatens the
            widespread deployment of Data Grids in domains such
            as health-care and finance. We propose a unique approach
            for obtaining knowledge by way of a data mining
            model from a Data Grid, while ensuring that the data
            is cryptographically safe. This is made possible by an innovative,
            yet natural generalization for the accepted
            trusted third party model and a new privacy-preserving
            data mining algorithm that is suitable for Grid-scale systems.
            The algorithm is asynchronous, involves no global
            communication patterns, and dynamically adjusts to
            changes in the data or to the failure and recovery of resources.
            To the best of our knowledge, this is the first
            privacy-preserving mining algorithm to possess these features.
            Simulations of thousands of resources prove that our
            algorithm quickly converges to the correct result while using
            reasonable communication. The simulations also prove
            that the effect of the privacy parameter on both the convergence
            time and the number of messages, is logarithmic.}}
}