CLM:面向轨迹发布的差分隐私保护方法  

CLM: differential privacy protection method for trajectory publishing

作　　者：王豪[1,2] 徐正全[1,2] 熊礼治[3] 王涛[1,2] WANG Hao XU Zheng-quan XIONG Li-zhi WANG Tao(State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China Collaborative Innovation Center for Geospatial Technology, Wuhan University, Wuhan 430079, China School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China)

机构地区：[1]武汉大学测绘遥感信息工程国家重点实验室,湖北武汉430079 [2]武汉大学地球空间信息技术协同创新中心,湖北武汉430079 [3]南京信息工程大学计算机与软件学院,江苏南京210044

出　　处：《通信学报》2017年第6期85-96,共12页Journal on Communications

基　　金：国家自然科学基金资助项目(No.41671443);武汉市应用基础研究计划基金资助项目(No.2016010101010024);中美计算机科学研究中心开放基金资助项目(No.KJR16228);南京信息工程大学人才引进基金资助项目(No.2016r055)~~

摘　　要：针对现有轨迹差分隐私保护发布方法面临的独立噪声容易被滤除的问题,提出一种轨迹差分隐私发布方法——CLM。CLM提出一种相关性拉普拉斯机制,利用高斯噪声通过特定的滤波器,产生与原始轨迹序列自相关函数一致的相关性噪声序列,叠加到原始轨迹中并发布。实验结果表明,与现有的轨迹差分隐私保护发布方法相比,CLM能够达到更高的隐私保护强度并能保证较好的数据可用性。In order to solve the problem existing in differential privacy preserving publishing methods that the indepen-dent noise was easy to be filtered out, a differential privacy publishing method for trajectory data （CLM）, was proposed. A correlated Laplace mechanism was presented by CLM, which let Gauss noises pass through a specific filter to produce noise whose auto-correlation function was similar with original trajectory series. Then the correlated noise was added to the original track and the perturbed track was released. The experimental results show that the proposed method can achieve higher privacy protection and guarantee better data utility compared with existing differential privacy preserving publishing methods for trajectory data.

关 键 词：轨迹发布 隐私保护 差分隐私 相关性拉普拉斯 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]