面向数据发布和分析的差分隐私保护

摘要

As the emergence and development of application requirements such as data analysisand data publication,a challenge to those applications is to protect private data and prevent sensitiveinformation from disclosure.However,most existing methods based on犽-anonymity or partition-based have serious limitations because they only preserve individual privacy under special assumptionof adversary’s background knowledge.Differential privacy has emerged as a new paradigm forprivacy protection with strong privacy guarantees against adversaries with arbitrary backgroundknowledge.This paper surveys the state of the art of differential privacy for data publication andanalysis.The mechanisms and properties of this model are described,while our focuses are puton private data releases in terms of histogram and partition techniques,and analysis based onregression skills.Following the comprehensive comparison and analysis of existing works,futureresearch directions are put forward.%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。基于犽-匿名或者划分的隐私保护方法，只适应特定背景知识下的攻击而存在严重的局限性。差分隐私作为一种新出现的隐私保护框架，能够防止攻击者拥有任意背景知识下的攻击并提供有力的保护。文中对差分隐私保护领域已有的研究成果进行了总结，对该技术的基本原理和特征进行了阐述，重点介绍了当前该领域的研究热点：差分隐私下基于直方图的发布技术、基于划分的发布技术以及回归分析技术。在对已有技术深入对比分析的基础上，指出了差分隐私保护技术的未来发展方向。