文章基本信息

标题：Privacy Preserving Two-Layer Decision Tree Classifier for Multiparty Databases
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作者：Alka Gangrade ; Ravindra Patel
期刊名称：International Journal of Computer and Information Technology
印刷版ISSN：2279-0764
出版年度：2012
卷号：1
期号：1
页码：77
出版社：International Journal of Computer and Information Technology
摘要：Privacy protection is one of the important problems in data mining. The growth of the Internet has triggered incredible opportunities for cooperative computation, where people are jointly conducting computation tasks based on the private inputs they each supplies. These computations could occur between mutually un-trusted parties or even between competitors. Today, to conduct such computations, one entity must usually know the inputs from all the participants, however if nobody can be trusted enough to know all the inputs, privacy will become a primary concern. Our two layer protocol uses an Un-trusted Third Party (UTP). We study how to build privacy preserving two-layer decision tree classifier, where database is horizontally partitioned and communicate their intermediate results to the UTP not their private data. In our protocol, an UTP allows well- designed solutions that meet privacy constraints and achieve acceptable performance.
关键词：Privacy preserving; Un-trusted Third Party; decision ; tree.