摘要
尽管大数据分析在许多领域展现出巨大价值,但目前大数据的发展仍然面临着诸多问题,隐私保护便是公认的关键问题之一.在大数据背景下,数据间存在着复杂的关联性.为防止攻击者利用先验知识获取隐私,Dwork提出了差分隐私概念.近年来,差分隐私受到了广泛关注,成为有望解决数据隐私问题的一个重要研究方向.值得注意的是,在诸如执行MapReduce计算的Airavat等系统中,差分隐私技术仅用于构件层面.显然,保护单个构件的隐私并不意味着保护了整个系统的隐私.为此,国内外许多学者从系统层面研究了隐私保护.文章在简要回顾传统差分隐私提出的背景、定义及理论方面的进展后,从形式化方法的视角,综述概率系统差分隐私的最新研究进展和研究方向,以期促进该领域的进一步研究.
Although big data analysis has shown great value in some fields,the current development of big data is still faced with many problems.Among others,privacy preserving is recognized as a key issue.In the context of big data,there is an intricate relevance among data.To prevent an attacker from acquiring privacy by using a priori knowledge,Dwork proposed the concept of differential privacy.In recent years,differential privacy has been broadly concerned and has become an important research field to solve the privacy problem in the context of big data.However,it is worth noting that,in some systems such as Airavat performing MapReduce computations,the differential privacy technology is only used in some components.Clearly,preserving the privacy of components is not sufficient for the privacy preserving of an entire system.To this end,some scholars have made many efforts to ensure privacy at the system level.In this paper,after briefly reviewing the background,definition,and theoretical progress of the classical differential privacy,we survey the latest developments and research directions of differential privacy in probabilistic systems from the perspective of formal methods,with a view to promoting further research in this field.
作者
曹永知
CAO Yong-zhi(Key Laboratory of High Confidence Software Technologies(MOE),School of Electronics Engineering and Computer Science,Peking University,Beijing 100871,China)
出处
《广州大学学报（自然科学版）》
CAS
2019年第4期75-82,共8页
Journal of Guangzhou University:Natural Science Edition
基金
国家自然科学基金资助项目(61772035)
关键词
隐私保护
差分隐私
形式化方法
概率自动机
概率标号迁移系统
概率进程代数
马尔可夫链
模型检测
privacy preserving
differential privacy
formal methods
probabilistic automaton
probabilistic labelled transition system
probabilistic process algebra
Markov chain
model checking
