计算机科学 ›› 2017, Vol. 44 ›› Issue (12): 38-41.doi: 10.11896/j.issn.1002-137X.2017.12.007
夏英,毛鸿睿,张旭,裴海英
XIA Ying, MAO Hong-rui, ZHANG Xu and BAE Hae-young
摘要： 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。
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