Privacy Preserving ID3 Algorithm over Horizontally Partitioned Data

Abstract

For the problem of decision tree classification with privacy concerns, we propose several efficient secure multi-party computation protocols to construct a privacy preserving ID3 algorithm over horizontally partitioned data among multiple parties. Our algorithm presents the first solution to privacy preserving decision tree classification among more than two parties. We also make a performance comparison with the existing solution, which is only applicable to the twoparty case. The result shows that our solution has a significantly better performance.