@INPROCEEDINGS{Gilburd_04,
    AUTHOR      = {Bobi Gilburd and Assaf Schuster and Ran Wolff},
    TITLE       = {{k-TTP: a New Privacy Model for Large-scale Distributed Environments}},
    BOOKTITLE   = {{10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}},
    YEAR        = {2004},
    EDITOR      = {},
    PAGES       = {563--568},
    PUBLISHER   = {},
    VOLUME      = {},
    NUMBER      = {},
    SERIES      = {},
    ADDRESS     = {Seattle, WA},
    MONTH       = {August},
    NOTE        = {},
    KEYWORDS    = {},
    ISBN        = {},
    URL         = {http://www.cs.technion.ac.il/~assaf/publications/MP4ARM_KDD.pdf},
    ABSTRACT    = {Secure multiparty computation allows parties to jointly com-
           pute a function of their private inputs without revealing
           anything but the output. Theoretical results provide
           a general construction of such protocols for any function.
           Protocols obtained in this way are, however, ine±cient, and
           thus, practically speaking, useless when a large number of
           participants are involved.
           The contribution of this paper is to define a new privacy
           model - k-privacy - by means of an innovative, yet natural
           generalization of the accepted trusted third party model.
           This allows implementing cryptographically secure e±cient
           primitives for real-world large-scale distributed systems.
           As an example for the usefulness of the proposed model,
           we employ k-privacy to introduce a technique for obtaining
           knowledge - by way of an association-rule mining algorithm
           - from large-scale Data Grids, while ensuring that the pri-
           vacy is cryptographically secure.},
}