Name
Abstract | ||
|---|---|---|
The advent of public use statistical database query systems raises problems of controlling inference of confidential information. Some of these problems are new while others present new challenges in terms of scalability of computational algorithms. We examine three problems: obtaining exact interval estimates of data withheld to address confidentiality concerns; confidentiality issues associated with the release of ordinary least squares regression models; and, confidentiality issues associated with the release of spatial statistical models based on ordinary kriging. For the first, we treat the database as one large multi-dimensional contingency table (large number of records, large dimension). |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/1-4020-8128-6_1 | RESEARCH DIRECTIONS IN DATA AND APPLICATIONS SECURITY XVIII |
Keywords | Field | DocType |
contingency table,spatial statistics,interval estimation,ordinary least squares regression | Data mining,Confidentiality,Inference,Fiducial inference,Computer science,Ordinary least squares,Contingency table,Statistical model,Statistical database,Scalability | Conference |
Volume | ISSN | Citations |
144 | 1571-5736 | 2 |
PageRank | References | Authors |
0.49 | 3 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lawrence H. Cox | 1 | 33 | 5.63 |