基于隐私保护的决策树模型

在分布式环境下，实现隐私保护的数据挖掘，已成为该领域的研究热点。文中着重研究在垂直分布数据中，实现隐私保护的决策树分类模型。该模型创建新型的隐私保护决策树，即由在茫然半诚实方存储的全局决策表和各站点存储的局部决策树组成，并结合索引数组和秘密数据比较协议，实现在不泄漏原始信息的前提下决策树的生成和分类。经过理论分析和实验验证，证明该模型具有较好的安全性、准确性和适用性。

Decision-Tree Model Research Based on Privacy-Preserving

How to realize privacy-preserving data mining becomes a research hotspot in a distributed environment. A model is proposed to realize privacy-preserving decision-tree classifying when data are vertically partitioned. In this model, a privacy-preserving decision-tree is proposed, which is composed of Global-Table stored by an obvious semi-honest partner and several local decision-trees stored by different sites. By using indexed array and private data comparison protocol, decision-tree generation and classification can be realized without uncovering the original information. Theoretical analysis and experimental results demonstrate the proposed model provides good capabilities of privacies preserving, accuracy and efficiency.