摘要
针对差分隐私在数据挖掘中的最新成果进行了研究,介绍了差分隐私保护的定义和实现机制,分析了差分隐私在模式挖掘、分类和聚类中的相关研究,着重解析了部分重要技术的实现原理,对比分析了其优缺点和算法复杂度。最后,展望了差分隐私在动态数据发布和大数据环境下的研究方向。
The latest results of differential privacy in data mining were surveyed. The basic concepts of differential pri- vacy were introduced. It analyzes the differential privacy＇s research in pattern mining, classification and cluster. It was focused that on the analysis of the principle of some important technology to achieve. And also it was made that com- parative analysis of its strengths and weaknesses and algorithm complexity. Finally, the future research of difference pri- vacy under the dynamic data publication and big data environments was discussed.
作者
康海燕
马跃雷
KANG Hai-yan MA Yue-lei(School of Information Management, Beijing Information Science and Technology University, Beijing 100192, China School of Computer Science, Beijing Information Science and Technology University, Beijing 100192, China)
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2017年第3期16-23,31,共9页
Journal of Shandong University(Natural Science)
基金
国家自然科学基金资助项目(61370139)
北京市社会科学基金项目(15JGB099)
北京市优秀人才培养资助项目(2013E005007000001)
关键词
隐私保护
差分隐私
数据挖掘
信息安全
privacy preserving
differential privacy
data mining
information security
