Secure analysis of distributed chemical databases without data integration

Alan F. Karr, Jun Feng, Xiaodong Lin, Ashish P. Sanil, S. Stanley Young, Jerome P. Reiter

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)739-747
Number of pages9
JournalJournal of Computer-Aided Molecular Design
Issue number9-10
StatePublished - Sep 2005
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Drug Discovery
  • Computer Science Applications
  • Physical and Theoretical Chemistry


  • Chemical database
  • Distributed data
  • Regression model
  • Secure multi-party computation


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