.png){kind=icon}
返回
Privacy-preserving collaborative data mining
.png){kind=icon}
PRE
AI
DOI：10.1109/MCI.2008.919071.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Data collection is a necessary step in data raining process. Due to privacy reasons, collecting data from different paries becomes difficult. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. How multiple parties collaboratively conduct data mining without breaching data privacy presents a challenge. The objective of this paper is to provide solutions for privacy-preserving collaborative data mining problems. In particular, we illustrate how to conduct privacy-preserving naive Bayesian classification which is one of the data mining tasks. To measure the privacy level for privacy-preserving schemes, we propose a definition of privacy and show that our Solutions preserve data privacy.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11.2
论文数:
613
被引数:
3.1K
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏Fatigue crack growth properties of Ni-base alloys in high pressure hydrogen at room temperature.
OAAI
OAAI分享收藏
查看更多内容