Privacy-preserving data mining (PPDM) techniques aim to construct efficient data mining algorithms while main- taining privacy. Statistical disclosure limitation (SDL) tech- niques 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 prob- lem of performing a "secure" logistic regression on pooled data collected separately by several parties without directly combining their databases. We describe "secure" Newton- Raphson protocol for binary logistic regression in the case of horizontally and vertically partitioned databases using secure-mulity party computation.
['Aleksandra Slavkovic', 'Yuval Nardi', 'M. Tibbits']
"Secure" Logistic Regression of Horizontally and Vertically Partitioned Distributed Databases