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.
Original language | English |
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Title of host publication | Financial Cryptography and Data Security |
Subtitle of host publication | FC 2017 International Workshops, WAHC, BITCOIN, VOTING, WTSC, and TA, Sliema, Malta, April 7, 2017, Revised Selected Paper |
Publisher | Springer-Verlag |
Pages | 75-90 |
Volume | 10323 |
ISBN (Print) | 978-3-319-70277-3 |
DOIs | |
Publication status | Published - 7 Apr 2017 |
Event | 5th Workshop on Encrypted Computing and Applied Homomorphic Cryptography - Sliema, Malta Duration: 7 Apr 2017 → 7 Apr 2017 https://www.chi.uni-hannover.de/wahc17 |
Publication series
Name | Lecture Notes in Computer Science |
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Workshop
Workshop | 5th Workshop on Encrypted Computing and Applied Homomorphic Cryptography |
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Abbreviated title | WAHC'17 |
Period | 7/04/17 → 7/04/17 |
Internet address |