Year: 2002
Type: article
Abstract: This paper studies how to build a decision tree clas-sifier under the following scenario: a database is ver-tically partitioned into two pieces, with one piece owned by Alice and the other piece owned by Bob. Alice and Bob want to build a decision tree classi-fier based on such a database, but due to the privacy constraints, neither of them wants to disclose their private pieces to the other party or to any third party. We present a protocol that allows Alice and Bob to conduct such a classifier building without having to compromise their privacy. Our protocol uses an un-trusted third-party server, and is built upon a useful building block, the scalar product protocol. Our so-lution to the scalar product protocol is more efficient than any existing solutions.
Authors: Wenliang Du, Zhijun Zhan
Institution: Syracuse University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1574534563