Private Outsourced Kriging Interpolation. / Alderman, James; Curtis, Benjamin; Farràs Ventura, Oriol; Martin, Keith; Ribes-González, Jordi.

Financial Cryptography and Data Security: FC 2017 International Workshops, WAHC, BITCOIN, VOTING, WTSC, and TA, Sliema, Malta, April 7, 2017, Revised Selected Paper. Vol. 10323 Springer-Verlag, 2017. p. 75-90 (Lecture Notes in Computer Science).

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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 languageEnglish
Title of host publicationFinancial Cryptography and Data Security
Subtitle of host publicationFC 2017 International Workshops, WAHC, BITCOIN, VOTING, WTSC, and TA, Sliema, Malta, April 7, 2017, Revised Selected Paper
PublisherSpringer-Verlag
Pages75-90
Volume10323
ISBN (Print)978-3-319-70277-3
DOIs
StatePublished - 7 Apr 2017
Event5th Workshop on Encrypted Computing and Applied Homomorphic Cryptography -

Publication series

NameLecture Notes in Computer Science

Workshop

Workshop5th Workshop on Encrypted Computing and Applied Homomorphic Cryptography
Abbreviated titleWAHC'17
Period7/04/177/04/17
Internet address
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 29431335