mining multiple private databases using a knn classifier
Modern electronic communication has collapsed geographical boundaries for global information sharing but often at the expense of data security and privacy boundaries. Distributed privacy preserving data mining tools are increasingly becoming critical for mining multiple databases with a minimum information disclosure. We present a framework including a general model as well as multi-round algorithms for mining horizontally partitioned databases using a privacy preserving k Nearest Neighbor ( k NN) classifier. A salient feature of our approach is that it offers a trade-off between accuracy, efficiency and privacy through multi-round protocols.
privacy classification  k  nearest neighbor distributed databases