| 朴素贝叶斯分类中的隐私保护方法研究 |
| |
| 引用本文： | 张鹏,唐世渭.朴素贝叶斯分类中的隐私保护方法研究[J].计算机学报,2007,30(8):1267-1276. |
| |
| 作者姓名： | 张鹏 唐世渭 |
| |
| 作者单位： | 1. 中国电信股份有限公司北京研究院,北京,100035
2. 北京大学信息科学技术学院,北京,100871 |
| |
| 基金项目： | 国家自然科学基金
,
北京市科学技术委员会博士论文专项基金 |
| |
| 摘 要： | 数据挖掘中的隐私保护方法,试图在不精确访问原始数据详细信息的条件下,挖掘出准确的模式与规则.围绕着分类挖掘中的隐私保护问题展开研究,给出了一种基于数据处理和特征重构的朴素贝叶斯分类中的隐私保护方法.分别提出了一种针对枚举类型的隐私数据处理与特征重构方法--扩展的部分隐藏随机化回答(Extended Randomized Response with Partial Hiding,ERRPH)方法和一种针对数值类型的隐私数据处理与特征重构方法--转换的随机化回答(Transforming Randomized Response,TRR)方法,并在此基础上实现了一个完整的隐私保护的朴素贝叶斯分类算法.理论分析和实验结果均表明:朴素贝叶斯分类中基于ERRPH和TRR的隐私保护方法具有很好的隐私性、准确性、高效性和适用性.
|
| 关 键 词： | 数据挖掘 隐私保护 朴素贝叶斯分类 随机处理 特征重构 朴素贝叶斯分类算法 保护方法 研究 Classification Naive Bayes Preserving 高效性 隐私性 结果 实验 分析 理论 Transforming 转换 值类型 对数 Extended Response Partial 随机化回答 |
| 修稿时间： | 2007-03-05 |
Privacy Preserving Naive Bayes Classification |
| |
| ZHANG Peng,TANG Shi-Wei.Privacy Preserving Naive Bayes Classification[J].Chinese Journal of Computers,2007,30(8):1267-1276. |
| |
| Authors: | ZHANG Peng TANG Shi-Wei |
| |
| Affiliation: | 1.Beijing Research Institute, China Telecom Corporation Limited, Beijing 100035;2.School of Electronics Engineering and Computer Science, Peking University, Beijing 100871 |
| |
| Abstract: | Privacy preserving data mining is to discover accurate patterns without precise access to the original data. This paper focuses on privacy preserving classification, and presents a privacy preserving Naive Bayes classification approach based on data randomization and feature reconstruction. An ERRPH (Extended Randomized Response with Partial Hidding) method and a TRR (Transforming Randomized Response) method are respectively presented for enumerated data and numerical data. Then, a privacy preserving Naive Bayes classification algorithm is implemented based on those methods. Theoretical analyses show that it can provide better privacy, accuracy, efficiency, and applicability. The effectiveness is also verified by experiments. |
| |
| Keywords: | data mining privacy preservation Naive Bayes classification data randomization feature reconstruction |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
