华中科技大学学报（自然科学版）2018，Vol.46Issue(1)：32-36,5.DOI:10.13245/j.hust.180107
面向轨迹聚类的差分隐私保护方法
Differential privacy preserving method for trajectory clustering
摘要
Abstract
As existing privacy preserving mechanisms for trajectory clustering are still faced with the problems of narrow applicability,low-level utility,which are difficult to imply in real scenarios,a differential privacy preserving mechanism was proposed to support trajectory clustering.Firstly,general framework model of typical trajectory clustering algorithms was given and the definition of differential privacy was introduced according to the framework.Then,the probability density function of two-dimensional Laplace noise satisfying the above definitions was derived.Finally,the noise from Cartesian coordinate syst em was transformed to Polar coordinate system to imply it efficiently.Experimental results show that compared with present methods,the proposed mechanism has general application and better cluster performance under the same preserving intensity.关键词
数据挖掘/轨迹聚类/隐私保护/差分隐私/二维拉普拉斯噪声Key words
data mining/trajectory clustering/privacy preserving/differential privacy/two-dimensional Laplace noise分类
信息技术与安全科学引用本文复制引用
王豪,徐正全..面向轨迹聚类的差分隐私保护方法[J].华中科技大学学报（自然科学版）,2018,46(1):32-36,5.基金项目
国家自然科学基金资助项目(41671443) （41671443）
武汉市应用基础研究计划资助项目(2016010101010024). （2016010101010024）
