摘要
为解决轨迹差分隐私保护中存在的隐私预算与服务质量等问题,提出了一种融合预测扰动的轨迹差分隐私保护机制。首先,利用马尔可夫链和指数扰动方法预测满足差分隐私和时空安全的扰动位置,并引入服务相似地图检测该位置的可用性;如果预测成功,则直接采用预测位置替代差分扰动的位置,以降低连续查询的隐私开销并提高服务质量。在此基础上,设计基于w滑动窗口的轨迹隐私预算分配机制,确保轨迹中任意连续的w次查询满足ε-差分隐私,解决连续查询的轨迹隐私问题。此外,基于敏感度地图设计一种隐私定制策略,通过自定义语义位置的隐私敏感度,实现隐私预算的量身定制,从而进一步提高其利用率。最后,利用真实数据集对所提方案进行实验分析,结果显示所提方案提供了更好的隐私保护水平和服务质量。
To address the issues of privacy budget and quality of service in trajectory differential privacy protection,a trajectory differential privacy mechanism integrating prediction disturbance was proposed.Firstly,Markov chain and exponential perturbation method were used to predict the location which satisfies the differential privacy and temporal and spatial security,and service similarity map was introduced to detect the availability of the location.If the prediction was successful,the prediction location was directly used to replace the location of differential disturbance,to reduce the privacy cost of continuous query and improve the quality of service.Based on this,the trajectory privacy budget allocation mechanism based on w sliding window was designed to ensure that any continuous w queries in the trajectory meet theε-differential privacy and solve the trajectory privacy problem of continuous queries.In addition,a privacy customization strategy was designed based on the sensitivity map.By customizing the privacy sensitivity of semantic location,the privacy budget could be customized to improve its utilization.Finally,the validity of the scheme was verified by real data set experiment.The results illustrate that it offers the better privacy and quality of service.
作者
叶阿勇
孟玲玉
赵子文
刁一晴
张娇美
YE Ayong;MENG Lingyu;ZHAO Ziwen;DIAO Yiqing;ZHANG Jiaomei(College of Mathematics and Informatics,Fujian Normal University,Fuzhou 350007,China;Fujian Provincial Key Laboratory of Network Security and Cryptology,Fuzhou 350007,China)
出处
《通信学报》
EI
CSCD
北大核心
2020年第4期123-133,共11页
Journal on Communications
基金
国家自然科学基金资助项目(No.61972096,No.61872088,No.61872090)
福建省自然科学基金资助项目(No.2018J01780)。
关键词
位置隐私
轨迹隐私
差分隐私
隐私累积
location privacy
trajectory privacy
differential privacy
privacy accumulation
