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 (kNN) classifier. A salient feature of our approach is that it offers a trade-off between accuracy, efficiency and privacy through multi-round protocols. Copyright 2007 ACM.
Mendeley saves you time finding and organizing research
Choose a citation style from the tabs below