Name
Abstract | ||
|---|---|---|
Reluctance of data owners to share their possibly confidential or proprietary data with others who own related databases is a serious impediment to conducting a mutually beneficial data mining analysis. We address the case of vertically partitioned data -- multiple data owners/agencies each possess a few attributes of every data record. We focus on the case of the agencies wanting to conduct a linear regression analysis with complete records without disclosing values of their own attributes. This paper describes an algorithm that enables such agencies to compute the exact regression coefficients of the global regression equation and also perform some basic goodness-of-fit diagnostics while protecting the confidentiality of their data. In more general settings beyond the privacy scenario, this algorithm can also be viewed as method for the distributed computation for regression analyses. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1145/1014052.1014139 | KDD |
Keywords | Field | DocType |
multiple data owner,exact regression coefficient,partitioned data,beneficial data mining analysis,global regression equation,data owner,linear regression analysis,proprietary data,regression modelling,regression analysis,data record,regression,secure multi party computation,data mining,regression equation,relational database,distributed computing,data integration,data confidentiality,goodness of fit,data integrity,linear regression,computation | Data integration,Data mining,Multiple data,Secure multi-party computation,Regression,Confidentiality,Regression analysis,Computer science,Artificial intelligence,Machine learning,Linear regression,Computation | Conference |
ISBN | Citations | PageRank |
1-58113-888-1 | 46 | 3.00 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashish Sanil | 1 | 152 | 12.81 |
| Alan F. Karr | 2 | 1005 | 76.93 |
| Xiaodong Lin | 3 | 80 | 5.34 |
| Jerome P. Reiter | 4 | 216 | 22.12 |