Privacy-preserving kriging interpolation on partitioned data
KNOWLEDGE-BASED SYSTEMS, cilt.62, ss.38-46, 2014 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 62
- Basım Tarihi: 2014
- Doi Numarası: 10.1016/j.knosys.2014.02.017
- Dergi Adı: KNOWLEDGE-BASED SYSTEMS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.38-46
- Anahtar Kelimeler: Privacy, Kriging, Partitioned data, Prediction, Geo-statistics, DATABASES
- Ankara Üniversitesi Adresli: Evet
Özet
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.