Classification Spanning Private Databases

In this paper, we study the classification problem involving information spanning multiple private databases. The privacy challenges lie in the facts that data cannot be collected in one place and the classifier itself may disclose private information. We present a novel solution that builds the same decision tree classifier as if data are collected in a central place, but preserves the privacy of participating sites.