Privacy-preserving kriging interpolation on partitioned data

TUĞRUL B., POLAT H.

KNOWLEDGE-BASED SYSTEMS, vol.62, pp.38-46, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 62
  • Publication Date: 2014
  • Doi Number: 10.1016/j.knosys.2014.02.017
  • Journal Name: KNOWLEDGE-BASED SYSTEMS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.38-46
  • Keywords: Privacy, Kriging, Partitioned data, Prediction, Geo-statistics, DATABASES
  • Ankara University Affiliated: Yes

Abstract

Kriging is well-known, frequently applied method in geo-statistics. Its success primarily depends on the total number of measurements for some sample points. If there are sufficient sample points with measurements, kriging will reflect the surface accurately. Obtaining a sufficient number of measurements can be costly and time-consuming. Thus, different companies might obtain a limited number of measurements of the same region and want to offer predictions collaboratively. However, due to privacy concerns, they might hesitate to cooperate with each other.