摘要
基于k-匿名的位置隐私保护方法已被广泛研究,但该方法需要可信第三方且无法防止有背景信息对手的攻击,容易泄露位置隐私.针对这一难题,提出一种差分扰动的均衡增量近邻查询位置隐私保护方法.向用户的真实位置添加可控的拉普拉斯噪声,生成干扰位置,并将其作为锚点发送给位置服务商.采用均衡增量近邻查询算法,解决Space Twist算法中兴趣点围绕真实位置分布不均匀的问题,提高查询准确度.实验结果表明,在考虑数据通信量的情况下,该方法比Space Twist方法在查询相似度和响应时间上均具有较高优势,实现了隐私保护度与服务质量的平衡.
Location privacy protection method based on the k-anonymous has been widely studied,but it requires a trusted third party and can not prevent attacks from background information adversaries,making it easy to leak location privacy. In order to solve this problem,this paper proposed a homogeneous incremental nearest neighbor query method based on differential perturbation for location privacy protection,adding controllable Laplacian noise to the user＇s real location to generate an perturbation location and send it to the service provider. The homogeneous incremental neighbor query algorithm is used to address the problem that points of interest are found around the real position inhomogeneously in the SpaceTwist algorithm,which improves the query accuracy. The experimental results show that,under the condition of considering the data traffic,this method has a higher advantage than SpaceTwist method in query similarity and response time and achieves the balance between privacy protection and service quality.
作者
胡德敏
詹涵
HU De-min;ZHAN Han(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology, Shanghai 200093, Chin)
出处
《小型微型计算机系统》
CSCD
北大核心
2018年第7期1482-1486,共5页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(61170277
61472256)资助
上海市教委科研创新重点项目(12zz17)资助
上海市一流学科建设项目(S1201YLXK)资助
关键词
背景信息
差分隐私
位置隐私
增量近邻查询
background information
differential privacy
location privacy
incremental nearest neighbor query
