Private Outsourced Kriging Interpolation

James Alderman; Benjamin Curtis; Oriol Farràs Ventura; Keith Martin; Jordi Ribes-González

doi:10.1007/978-3-319-70278-0_5

Private Outsourced Kriging Interpolation

James Alderman, Benjamin Curtis, Oriol Farràs Ventura, Keith Martin, Jordi Ribes-González

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

71 Downloads (Pure)

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
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	https://doi.org/10.1007/978-3-319-70278-0_5
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

Workshop

Workshop	5th Workshop on Encrypted Computing and Applied Homomorphic Cryptography
Abbreviated title	WAHC'17
Period	7/04/17 → 7/04/17
Internet address	https://www.chi.uni-hannover.de/wahc17

Access to Document

10.1007/978-3-319-70278-0_5

Private_Outsourced_KrigingAccepted author manuscript, 381 KBLicence: Unspecified

Cite this

Alderman, J., Curtis, B., Farràs Ventura, O., Martin, K., & Ribes-González, J. (2017). Private Outsourced Kriging Interpolation. In 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, pp. 75-90). (Lecture Notes in Computer Science). Springer-Verlag. https://doi.org/10.1007/978-3-319-70278-0_5

@inproceedings{5846f7a6b5c54440868b37ee81e7f614,

title = "Private Outsourced Kriging Interpolation",

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. ",

author = "James Alderman and Benjamin Curtis and {Farr{\`a}s Ventura}, Oriol and Keith Martin and Jordi Ribes-Gonz{\'a}lez",

year = "2017",

month = apr,

day = "7",

doi = "10.1007/978-3-319-70278-0_5",

language = "English",

isbn = "978-3-319-70277-3",

volume = "10323",

series = "Lecture Notes in Computer Science",

publisher = "Springer-Verlag",

pages = "75--90",

booktitle = "Financial Cryptography and Data Security",

note = "5th Workshop on Encrypted Computing and Applied Homomorphic Cryptography, WAHC'17 ; Conference date: 07-04-2017 Through 07-04-2017",

url = "https://www.chi.uni-hannover.de/wahc17",

}

Alderman, J, Curtis, B, Farràs Ventura, O, Martin, K & Ribes-González, J 2017, Private Outsourced Kriging Interpolation. in 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, Lecture Notes in Computer Science, Springer-Verlag, pp. 75-90, 5th Workshop on Encrypted Computing and Applied Homomorphic Cryptography, 7/04/17. https://doi.org/10.1007/978-3-319-70278-0_5

Private Outsourced Kriging Interpolation. / Alderman, James; Curtis, Benjamin; Farràs Ventura, Oriol et al.
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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Private Outsourced Kriging Interpolation

AU - Alderman, James

AU - Curtis, Benjamin

AU - Farràs Ventura, Oriol

AU - Martin, Keith

AU - Ribes-González, Jordi

PY - 2017/4/7

Y1 - 2017/4/7

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-70278-0_5

DO - 10.1007/978-3-319-70278-0_5

M3 - Conference contribution

SN - 978-3-319-70277-3

VL - 10323

T3 - Lecture Notes in Computer Science

SP - 75

EP - 90

BT - Financial Cryptography and Data Security

PB - Springer-Verlag

T2 - 5th Workshop on Encrypted Computing and Applied Homomorphic Cryptography

Y2 - 7 April 2017 through 7 April 2017

ER -

Alderman J, Curtis B, Farràs Ventura O, Martin K, Ribes-González J. Private Outsourced Kriging Interpolation. In 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). doi: 10.1007/978-3-319-70278-0_5