"Secure" Logistic Regression of Horizontally and Vertically Partitioned Distributed Databases

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 neede...

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Bibliographic Details
Main Authors: Slavkovic, Aleksandra B., Nardi, Yuval, Tibbits, Matthew M.
Format: Conference Proceeding
Language:English
Subjects:
Data mining
Distributed databases
Linear regression
Logistics
Privacy
Protocols
Statistical analysis
Statistical distributions
Support vector machine classification
Support vector machines
Citations: Items that cite this one
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Summary: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.
ISSN:2375-9232
2375-9259
DOI:10.1109/ICDMW.2007.114