document: Privacy preserving regression modelling via distributed computation Reluctance of data owners to share their possibly confidential 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 .
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