Secure logistic regression of horizontally and vertically partitioned distributed databases

Aleksandra B. Slavkovic, Yuval Nardi, Matthew M. Tibbits

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

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" Newton-Raphson protocol for binary logistic regression in the case of horizontally and vertically partitioned databases using secure-mulity party computation.

Original languageEnglish (US)
Title of host publicationICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
Pages723-728
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE, United States
Duration: Oct 28 2007Oct 31 2007

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
CountryUnited States
CityOmaha, NE
Period10/28/0710/31/07

Fingerprint

Data mining
Logistics
Statistical methods

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Slavkovic, A. B., Nardi, Y., & Tibbits, M. M. (2007). Secure logistic regression of horizontally and vertically partitioned distributed databases. In ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops (pp. 723-728). [4476748] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDMW.2007.114
Slavkovic, Aleksandra B. ; Nardi, Yuval ; Tibbits, Matthew M. / Secure logistic regression of horizontally and vertically partitioned distributed databases. ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops. 2007. pp. 723-728 (Proceedings - IEEE International Conference on Data Mining, ICDM).
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Slavkovic, AB, Nardi, Y & Tibbits, MM 2007, Secure logistic regression of horizontally and vertically partitioned distributed databases. in ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops., 4476748, Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 723-728, 17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007, Omaha, NE, United States, 10/28/07. https://doi.org/10.1109/ICDMW.2007.114

Secure logistic regression of horizontally and vertically partitioned distributed databases. / Slavkovic, Aleksandra B.; Nardi, Yuval; Tibbits, Matthew M.

ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops. 2007. p. 723-728 4476748 (Proceedings - IEEE International Conference on Data Mining, ICDM).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Slavkovic AB, Nardi Y, Tibbits MM. Secure logistic regression of horizontally and vertically partitioned distributed databases. In ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops. 2007. p. 723-728. 4476748. (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDMW.2007.114