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  • 标题:Privacy Preserving Two-Layer Decision Tree Classifier for Multiparty Databases
  • 本地全文:下载
  • 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.
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