面向位置推荐的差分隐私保护方法

位置推荐服务能使用户更容易地获得周边的兴趣点信息,但也会带来用户位置隐私泄露的风险。为了避免位置隐私泄露带来的不利影响,提出一种面向位置推荐服务的差分隐私保护方法。在保持用户位置轨迹与签到频率特征的前提下,基于路径前缀树及其平衡程度采用均匀分配和几何分配两种方式进行隐私预算分配,然后根据隐私预算分配结果添加满足差分隐私的Laplace噪音。实验结果表明该方法能有效保护用户位置隐私,同时通过合理的隐私预算分配能减少差分隐私噪音对推荐质量的影响。

Differential Privacy Protection Method for Location Recommendation

Location recommendation service makes it easier for people to get surrounding information about Point of Interest (POI).However,there are some risks related to location privacy.In order to avoid the negative influence resulted from leaking location privacy,a privacy protection method for location recommendation service was proposed.On the premise of maintaining location trajectory and frequency characteristics of check-in,uniform distribution and geometry distribution were presented to control privacy budget allocation effectively based on path prefix tree (PP-Tree) and its balanced level,and thus the Laplace noise of differential privacy could be added according to the allocation result.Expe-riments indicate that this method can protect location privacy effectively.The impaction of differential privacy noise on the quality of location recommendation is reduced by reasonable privacy budget allocation.