【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。更多还原

【Abstract】 In order to solve the problem existing in differential privacy preserving publishing methods that the independent 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.更多还原