Private Outsourced Kriging Interpolation
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.
Oriol Farràs and Jordi Ribes-González were supported by the European Comission through H2020-ICT-2014-1-644024 “CLARUS” and H2020-DS-2015-1-700540 “CANVAS”, by the Government of Spain through TIN2014-57364-C2-1-R “SmartGlacis” and TIN2016-80250-R “Sec-MCloud”, by the Government of Catalonia through Grant 2014 SGR 537, and by COST Action IC1306. James Alderman was supported by the European Comission through H2020-ICT-2014-1-644024 “CLARUS”. Benjamin R. Curtis was supported by the UK EPSRC through EP/K035584/1 “Centre for Doctoral Training in Cyber Security at Royal Holloway”.
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