通信学报 ›› 2014, Vol. 35 ›› Issue (10): 200-209.doi: 10.3969/j.issn.1000-436x.2014.10.023

• 综述 • 上一篇    下一篇

面向频繁模式挖掘的差分隐私保护研究综述

丁丽萍1,卢国庆1,2   

  1. 1 中国科学院 软件研究所 基础软件国家工程研究中心,北京 100190
    2 中国科学院大学,北京 100190
  • 出版日期:2014-10-25 发布日期:2017-06-14
  • 基金资助:
    国家科技重大专项基金资助项目;中国科学院战略性科技先导专项基金资助项目

Survey of differential privacy in frequent pattern mining

Li-ping DING1,Guo-qing LU1,2   

  1. 1 National Engineering Research Center of Fundamental Software,Institute of Software,Chinese Academy of Sciences,Beijing 100190,China
    2 University of Chinese Academy of Sciences,Beijing 100190,China
  • Online:2014-10-25 Published:2017-06-14
  • Supported by:
    The National Science and Technology Major Program of China;The Strategic Technology Pilot Program of the Chinese Academy of Sciences

摘要:

频繁模式挖掘是数据挖掘的一个基本问题,其模式本身和相应计数都有可能泄露隐私信息。当前,差分隐私通过添加噪音使数据失真,有效实现了隐私保护的目的。首先介绍了差分隐私保护模型的理论基础;其次,详细综述了差分隐私下3种典型的频繁模式挖掘方法的最新研究进展,并进行对比性分析;最后对未来的研究方向进行了展望。

关键词: 差分隐私, 隐私保护, 频繁模式, 数据挖掘

Abstract:

Frequent pattern mining is an exploratory problem in the field of data mining.However,directly releasing the discovered frequent patterns and the corresponding true supports may reveal the individuals’ privacy.The state-of-the-art solution for this problem is differential privacy,which offers a strong degree of privacy protection by adding noise.Firstly,the theoretical basis of differential privacy was introduced.Then,three representative frequent pattern mining methods under differential privacy were summarized and compared in detail.Finally,some future research directions were discussed.

Key words: differential privacy, privacy protection, frequent pattern, data mining

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