Secure Logistic Regression of Horizontally and Vertically Partitioned Distributed Databases

Privacy-preserving data mining (PPDM) techniques aim to construct efficient data mining algorithms while maintaining privacy. Statistical disclosure limitation (SDL) techniques aim to preserve confidentiality but in contrast to PPDM techniques also aim to provide access to statistical data needed for “full” statistical analysis. We draw from both PPDM and SDL paradigms, and address the problem of performing a “secure” logistic regression on pooled data collected separately by several parties without directly combining their databases. We describe “secure” NewtonRaphson protocol for binary logistic regression in the case of horizontally and vertically partitioned databases using secure-mulity party computation.

• Type: Conference
• Year: 2007
• Where: ICDM
• Authors: Aleksandra B. Slavkovic, Yuval Nardi, Matthew M. Tibbits
• Keywords: Data Mining, ICDM 2007, Logistic Regression, Statistical Disclosure Limitation