title: Secure analysis of distributed chemical databases without data integration.
abstract: 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.
authors.affiliation: National Institute of Statistical Sciences Research, Triangle Park, NC 27709-4006, USA. karr@niss.org
authors.forename: Alan F
authors.initials: AF
authors.lastname: Karr
authors.forename: Jun
authors.initials: J
authors.lastname: Feng
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Lin
authors.forename: Ashish P
authors.initials: AP
authors.lastname: Sanil
authors.forename: S Stanley
authors.initials: SS
authors.lastname: Young
authors.forename: Jerome P
authors.initials: JP
authors.lastname: Reiter
mesh_terms: 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
chemical_list: D009930:Organic Chemicals; D014867:Water