k-匿名下通过本地差分隐私实现位置隐私保护

针对用户位置隐私保护过程中攻击者利用背景知识等信息发起攻击的问题,提出一种面向移动终端的位置隐私保护方法。该方案通过利用k-匿名和本地差分隐私技术进行用户位置保护,保证隐私和效用的权衡。结合背景知识构造匿名集,通过改进的Hilbert曲线对k-匿名集进行分割,使用本地差分隐私算法RAPPOR扰动划分后的位置集,最后将生成的位置集发送给位置服务提供商获取服务。在真实数据集上与已有的方案从用户位置保护、位置可用性和时间开销方面进行对比,实验结果显示,所提方案在确保LBS服务质量的同时,也增强了位置隐私保护的程度。

Location privacy protection through local differential privacy under k-anonymity

Aiming at the problem that attackers use background knowledge and other information to launch attacks in the process of protecting user location privacy, this paper proposed a location privacy protection method for mobile terminals. This solution used k-anonymity and local differential privacy technology to protect the user''s location to ensure the trade-off between privacy and utility. This scheme combined background knowledge to construct anonymity sets, used the improved Hilbert curve to segment the k-anonymous set, used local differential privacy algorithm RAPPOR to perturb the divided location sets, and finally sent the generated location sets to location service providers to obtain services. Comparing this scheme with existing real data set schemes in terms of user location protection, location availability and time overhead, the experimental results show that the proposed scheme not only ensures the quality of LBS service, but also enhances the degree of location privacy protection.