A distributed decision support algorithm that preserves personal privacy

Assuring confidentiality of personal information and preserving privacy are vital when data is harvested from multiple institutions for business decision-making. An algorithm that builds knowledge using statistics based on subject data from distributed sites that satisfy specified selection criteria is presented here. The algorithm maintains complete fidelity of information structures in the distributed data compared to the centralized equivalent. Heterogeneous data schemas across sites can be accommodated and thresholds can be set for global minimum saturation for attributes to participate in the prediction model building. Policies for inclusion and exclusion of non-exhaustive attributes among sites are introduced. Unification of attributes is introduced for homogenizing attribute values globally. Results of experiments using data from medical, higher education, and social domains elucidate the value of our algorithm in regulated industries, where shipping raw data outside parent institution is not practical.