Journal of Information Security Research ›› 2015, Vol. 1 ›› Issue (3): 224-229.

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Differential Privacy and Applications


  • Received:2015-12-14 Online:2015-12-15 Published:2016-01-18
  • About author:Dr Tianqing Zhu received her BEng and MEng degrees from Wuhan University, China, in 2000 and 2004, respectively, and a PhD degree from Deakin University in Computer Science, Australia, in 2014. Dr Tianqing Zhu is currently a continuing teaching scholar in the School of Information Technology, Deakin University, Melbourne, Australia. Before joining Deakin University, she served as a lecturer in Wuhan Polytechnic University, China from 2004 to 2011. Her research interests include privacy preserving, data mining and network security. She has won the best student paper award in PAKDD 2014 and was invited to give a tutorial on differential privacy in PAKDD 2015.



  1. 华中科技大学计算机学院
  • 通讯作者: 朱天清
  • 作者简介:朱天清 澳大利亚墨尔本迪肯大学信息技术学院讲师,主要研究方向为隐私保护. 何木青 博士研究生,主要研究方向为隐私保护. 邹德清 博士,教授,博士生导师,主要研究方向为系统安全、网络攻防、大数据安全、容错计算.

Abstract: As the emergence and development of application requirements such as data analysis and data publication, a challenge to those applications is to protect private data and prevent sensitive information from disclosure. With the highspeed development of information and network, big data has become a hot topic in both the academic and industrial research, which is regarded as a new revolution in the field of information technology. However, it brings about not only significant economic and social benefits, but also great risks and challenges to individuals` privacy protection and data security. People on the Internet leave many data footprint with cumulatively and relevance. Personal privacy information can be found by gathering data footprint in together.Malicious people use this information for fraud. It brings many trouble or economic loss to personal life.Privacy preserving, especially in data release and data mining, is a hot topic in the information security field. Differential privacy has grown rapidly recently due to its rigid and provable privacy guarantee. We analyze the advantage of differential privacy model relative to the traditional ones, and review other applications of differential privacy in various fields and discuss the future research directions. Following the comprehensive comparison and analysis of existing works, future research directions are put forward.

Key words: differential privacy, data release, data mining, statistical query, privacy preserving, big data

摘要: 随着数据分析和发布等应用需求的出现和发展,如何保护隐私数据和防止敏感信息泄露成为当前面临的重大挑战.信息化和网络化的高速发展使得大数据成为当前学术界和工业界的研究热点,是IT业正在发生的深刻技术变革.但它在提高经济和社会效益的同时,也为个人和团体的隐私保护以及数据安全带来极大风险与挑战.大数据隐私以及数据挖掘中的隐私问题是信息安全领域目前的一个研究热点.差分隐私作为一种严格的和可证明的隐私定义,自诞生以来便受到了相当关注.介绍了隐私保护的变化和发展,对差分隐私保护技术的基本原理和特征进行了阐述,分析了差分隐私保护模型相对于传统安全模型的优势,并对其在数据发布与数据挖掘中的应用研究作了相应介绍.在对已有技术深入对比分析的基础上,展望了差分隐私保护技术的未来发展方向.

关键词: 差分隐私, 数据发布, 数据挖掘, 统计查询, 隐私保护, 大数据