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KDD
2004
ACM

Privacy preserving regression modelling via distributed computation

10 years 9 months ago
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. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications-Data Mining; I...
Ashish P. Sanil, Alan F. Karr, Xiaodong Lin, Jerom
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2004
Where KDD
Authors Ashish P. Sanil, Alan F. Karr, Xiaodong Lin, Jerome P. Reiter
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