Secure logistic regression of horizontally and vertically partitioned distributed databases

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

doi:10.1109/ICDMW.2007.114

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 proceeding › Conference contribution

29 Scopus citations

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 language	English (US)
Title of host publication	ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
Pages	723-728
Number of pages	6
DOIs	https://doi.org/10.1109/ICDMW.2007.114
State	Published - 2007
Event	17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE, United States Duration: Oct 28 2007 → Oct 31 2007

Publication series

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

Other

Other	17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
Country/Territory	United States
City	Omaha, NE
Period	10/28/07 → 10/31/07

All Science Journal Classification (ASJC) codes

Engineering(all)

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10.1109/ICDMW.2007.114

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

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title = "Secure logistic regression of horizontally and vertically partitioned distributed databases",

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.",

author = "Slavkovic, {Aleksandra B.} and Yuval Nardi and Tibbits, {Matthew M.}",

year = "2007",

doi = "10.1109/ICDMW.2007.114",

language = "English (US)",

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series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

pages = "723--728",

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note = "17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 ; Conference date: 28-10-2007 Through 31-10-2007",

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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 proceeding › Conference contribution

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AU - Tibbits, Matthew M.

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N2 - 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.

AB - 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.

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T3 - Proceedings - IEEE International Conference on Data Mining, ICDM

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ER -

Secure logistic regression of horizontally and vertically partitioned distributed databases

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