Name
Abstract | ||
|---|---|---|
Kriging is a spatial interpolation algorithm which provides the best unbiased linear prediction of an observed phenomena by taking a weighted average of samples within a neighbourhood. It is widely used in areas such as geo-statistics where, for example, it may be used to predict the quality of mineral deposits in a location based on previous sample measurements. Kriging has been identified as a good candidate process to be outsourced to a cloud service provider, though outsourcing presents an issue since measurements and predictions may be highly sensitive. We present a method for the private outsourcing of Kriging interpolation using a tailored modification of the Kriging algorithm in combination with homomorphic encryption, allowing crucial information relating to measurement values to be hidden from the cloud service provider. |
Year | Venue | Field |
|---|---|---|
2017 | Financial Cryptography Workshops | Kriging,Data mining,Homomorphic encryption,Multivariate interpolation,Computer security,Computer science,Outsourcing,Linear prediction,Service provider,Weighted arithmetic mean,Cloud computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Alderman | 1 | 4 | 2.43 |
| Benjamin R. Curtis | 2 | 0 | 0.68 |
| Oriol Farràs | 3 | 197 | 15.52 |
| Keith M. Martin | 4 | 535 | 49.95 |
| Jordi Ribes-González | 5 | 0 | 0.68 |