Privacy-preserving geostatistics

Geo-statistics deals with spatial data and tries to find out relationship between locations and measured data. Methods used in geo-statistics interpolations rely on the principle that things are closer to each other more alike than the things are farther apart. Inverse distance weighting and kriging are most well-known and applied methods in geo-statistics. It is important to perform such methods without violating data confidentiality due to privacy reasons. Also, their accuracy depends on the total number of sample points. If there are insufficient sample points due to financial or privacy reasons, accuracy of the predictions produced by these methods may become unconvincing. There are cases in which institutions obtain measurements for the same or neighbor region. To create more accurate models, they may want to collaborate. However, they do not want to share their private data. In this thesis, privacy-preserving methods are proposed to provide inverse distance weighting- or kriging-based predictions for different data partitioning schemas including central server-based case. The proposed solutions are analyzed with respect to privacy, performance, and accuracy. Different sets of experiments are conducted using real data sets to analyze the proposed methods. Empirical outcomes show that the methods are able to provide accurate predictions while preserving privacy.