@INPROCEEDINGS{Sanil_04,
    AUTHOR      = {Ashish Sanil and Alan Karr and Xiaodong Lin and Jerome Reiter},
    TITLE       = {{Privacy Preserving Regression Modelling via Distributed Computation}},
    BOOKTITLE   = {{10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}},
    YEAR        = {2004},
    EDITOR      = {},
    PAGES       = {},
    PUBLISHER   = {},
    VOLUME      = {},
    NUMBER      = {},
    SERIES      = {},
    ADDRESS     = {Seattle, WA},
    MONTH       = {August},
    NOTE        = {},
    KEYWORDS    = {},
    ISBN        = {},
    URL         = {http://www.niss.org/dgii/TR/secure-coefficients.pdf},
    ABSTRACT    = {Reluctance of data owners to share their possibly confi-
           dential or proprietary data with others who own related
           databases is a serious impediment to conducting a mutually
           beneficial data mining analysis. We address the case of vertically
           partitioned data multiple data owners/agencies each
           possess a few attributes of every data record. We focus on
           the case of the agencies wanting to conduct a linear regression
           analysis with complete records without disclosing values
           of their own attributes. This paper describes an algorithm
           that enables such agencies to compute the exact regression
           coefficients of the global regression equation and also perform
           some basic goodness-of-fit diagnostics while protecting
           the confidentiality of their data. In more general settings beyond
           the privacy scenario, this algorithm can also be viewed
           as method for the distributed computation for regression analyses.},
}