Secure analysis of distributed chemical databases without data integration.
We present a method for performing statistically valid linear regressions on the union of distributed chemical databases that preserves confidentiality of those databases. The method employs secure multi-party computation to share local sufficient statistics necessary to compute least squares estimators of regression coefficients, error variances and other quantities of interest. We illustrate our method with an example containing four companies' rather different databases.
Karr
Alan F
AF
National Institute of Statistical Sciences Research, Triangle Park, NC 27709-4006, USA. karr@niss.org
Feng
Jun
J
Lin
Xiaodong
X
Sanil
Ashish P
AP
Young
S Stanley
SS
Reiter
Jerome P
JP
D000465:Algorithms; D016494:Computer Security; D016208:Databases, Factual; D016018:Least-Squares Analysis; D016014:Linear Models; D008956:Models, Chemical; D009930:Organic Chemicals / Q000737:chemistry; D012044:Regression Analysis; D012995:Solubility; D014867:Water
D009930:Organic Chemicals; D014867:Water