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 | |
| State | Published - Dec 1 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 | United States |
| City | Omaha, NE |
| Period | 10/28/07 → 10/31/07 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
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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
TY - GEN
T1 - Secure logistic regression of horizontally and vertically partitioned distributed databases
AU - Slavkovic, Aleksandra B.
AU - Nardi, Yuval
AU - Tibbits, Matthew M.
PY - 2007/12/1
Y1 - 2007/12/1
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.
UR - http://www.scopus.com/inward/record.url?scp=49549109804&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=49549109804&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2007.114
DO - 10.1109/ICDMW.2007.114
M3 - Conference contribution
AN - SCOPUS:49549109804
SN - 0769530192
SN - 9780769530192
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 723
EP - 728
BT - ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
ER -