个性化语义敏感轨迹数据发布算法

Personalized Semantic Sensitive Trajectory Data Publishing Algorithm

对于离线时用户轨迹发布方案中,大多都是将轨迹数据全部进行处理,通过添加噪声来满足差分隐私,缺少用户个性化的情况,导致数据可用性较低,且较少考虑位置点的语义信息.综合考虑上述情况,设计了一种个性化的语义敏感轨迹发布算法TSDP.该方案在进行发布时收集用户的语义敏感情况,且统计用户轨迹中频繁到访的语义位置点,同样考虑因时间段的不同而对敏感度得到影响,对其添加符合差分隐私的噪声.实验结果表明,与其他差分隐私保护方法相比,该方法在保证隐私的情况下,最大化了发布数据的可用性.

For offline user track publishing schemes,most of them process all track data,and add noise to meet differential privacy.The lack of user personalization leads to low data availability and less consideration of Semantic information of location points.Taking into account the above situation,a personalized semantic sensitive trajectory publishing algorithm TSDP has been designed.This scheme collects users′semantic sensitivity during publishing,and counts the frequently visited semantic location points in user trajectories.It also considers the impact on sensitivity due to different time periods,and adds noise that conforms to differential privacy.The experimental results show that compared with other differential privacy protection methods,this method maximizes the availability of published data while ensuring privacy.