差分隐私保护及其应用

A Survey on Differential Privacy and Applications

数据发布与数据挖掘中的隐私保护问题是目前信息安全领域的一个研究热点.作为一种严格的和可证明的隐私定义,差分隐私近年来受到了极大关注并被广泛研究.文中分析了差分隐私保护模型相对于传统安全模型的优势,对差分隐私基础理论及其在数据发布与数据挖掘中的应用研究进行综述.在数据发布方面,介绍了各种交互式和非交互式的差分隐私保护发布方法,并着重从精确度和样本复杂度的角度对这些方法进行了比较.在数据挖掘方面,阐述了差分隐私保护数据挖掘算法在接口模式和完全访问模式下的实现方式,并对这些算法的执行性能进行了分析.最后,介绍了差分隐私保护在其它领域的应用,并展望未来的研究方向.

Privacy preserving in data release and mining is a hot topic in the information security field currently.As a new privacy notion,differential privacy （DP） has grown in popularity recently due to its rigid and provable privacy guarantee.After analyzing the advantage of differential privacy model relative to the traditional ones,this paper surveys the theory of differential privacy and its application on two aspects,privacy preserving data release （PPDR） and privacy preserving data mining （PPDM）.In PPDR,we introduce the DP-based data release methodologies in interactive/non-interactive settings and compare them in terms of accuracy and sample complexity.In PPDM,we mainly summarize the implementation of DP in various data mining algorithms with interface-based/fully access-based modes as well as evaluating the performance of the algorithms.We finally review other applications of DP in various fields and discuss the future research directions.