通信学报 ›› 2017, Vol. 38 ›› Issue (Z1): 151-155.doi: 10.11959/j.issn.1000-436x.2017241

• 学术论文 • 上一篇    下一篇

差分隐私技术研究进展

高志强,王宇涛   

  1. 武警工程大学信息工程系,陕西 西安710086
  • 出版日期:2017-10-01 发布日期:2018-06-07
  • 作者简介:高志强(1989-),男,黑龙江齐齐哈尔人,武警工程大学博士生,主要研究方向为隐私计算、深度神经网络、群智能优化等。|王宇涛(1989-),男,贵州贵阳人,武警工程大学硕士生,主要研究方向为大数据挖掘。
  • 基金资助:
    国家自然科学基金资助项目(61402529)

Survey on differential privacy and its progress

Zhi-qiang GAO,Yu-tao WANG   

  1. Department of Information Engineering,Engineering University of PAP,Xi’an 710086,China
  • Online:2017-10-01 Published:2018-06-07
  • Supported by:
    The National Natural Science Foundation of China(61402529)

摘要:

随着大数据共享时代的到来,数据隐私保护问题也随之突显。自 2006 年提出以来,差分隐私技术在支持隐私保护的数据挖掘与数据发布方面得到了广泛研究。近年来,Google、Apple 等公司陆续将差分隐私技术应用于最新产品中,差分隐私技术再次成为学术界和产业界的焦点。首先,对传统集中式模型下的差分隐私技术进行综述,介绍了面向数据挖掘与数据发布的差分隐私技术。然后,着重对最新的基于本地差分隐私模型下的数据收集与数据分析进行阐述,涉及众包模型下的随机响应、BloomFilter、统计推断等技术。最后,对差分隐私技术面临的主要问题和解决方案进行总结。

关键词: 差分隐私, 数据发布, 数据挖掘, 机器学习, 众包, 隐私保护

Abstract:

With the arrival of the era of big data sharing,data privacy protection issues will be highlighted.Since its introduction in 2006,differential privacy technology has been widely researched in data mining and data publishing.In recent years,Google,Apple and other companies have introduced differential privacy technology into the latest products,and differential privacy technology has become the focus of academia and industry again.Firstly,the traditional centralized model of differential privacy was summarized,from the perspective of analysis of data mining and data released in the differential privacy way.Then the latest local differential privacy regarding data collection and data analysis based on the local model was described,involving crowdsourcing with random response technology,BloomFilter,statistical inference techniques.Finally,the main problems and solutions of differential privacy technology were summarized.

Key words: differential privacy, data publishing, data mining, machine learning, crowdsourcing, privacy protection

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