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Private Outsourced Kriging Interpolation


Jordi Ribes

Professor/a organitzador/a

Oriol Farrs


Universitat Rovira i Virgili


26-01-2017 12:00


The cloud computing paradigm offers data storage and processing services in external servers that lead to many economical and functional benefits. However, users are reluctant to outsource their data to the cloud because of security and privacy concerns. In this setting, the usage of cryptographic schemes is a natural option to provide data confidentiality and still harness the cloud benefits. In this talk we focus on securely outsourcing a particular interpolation method known as Kriging. 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 outsourcing Kriging interpolation in an efficient and secure fashion. By using a tailored modification of the Kriging algorithm in combination with homomorphic encryption, we allow crucial information relating to the measurement values to be hidden from the cloud service provider, while still allowing users to make use of the cloud computational power. This is a joint work with James Alderman, Benjamin Curtis, Oriol Farrs, and Keith Martin.


Laboratori 231