Name
Abstract | ||
|---|---|---|
Summary We present a method for performing statistically valid linear regressions on the union of distributed chemical databases that
preserves confidentiality of those databases. The method employs secure multi-party computation to share local sufficient statistics necessary to compute least squares estimators of regression coefficients, error variances
and other quantities of interest. We illustrate our method with an example containing four companies’ rather different databases.
|
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/s10822-005-9011-5 | Journal of computer-aided molecular design |
Keywords | Field | DocType |
chemical database,distributed data,regression model,secure multi-party computation | Data integration,Least squares,Data mining,Secure multi-party computation,Computer science,Regression analysis,Theoretical computer science,Sufficient statistic,Chemical database,Linear regression,Estimator | Journal |
Volume | Issue | ISSN |
19 | 9-10 | 0920-654X |
Citations | PageRank | References |
9 | 0.73 | 6 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alan F. Karr | 1 | 1005 | 76.93 |
| Jun Feng | 2 | 9 | 0.73 |
| Xiaodong Lin | 3 | 80 | 5.34 |
| Ashish Sanil | 4 | 152 | 12.81 |
| S Stanley Young | 5 | 63 | 4.80 |
| Jerome P. Reiter | 6 | 216 | 22.12 |