一种基于路网位置混淆的个性化位置隐私保护方案

Personalized Location Privacy Protection Scheme Based on Road Network Location Obfuscation

为了解决车载用户在路网中使用基于位置服务造成的位置隐私泄露问题,提出一种基于混淆的个性化位置隐私保护OPLPP方案.首先,基于差分隐私定义一个新的路网不可区分性RN-I概念,以此来衡量路网中位置的不可区分程度;其次,考虑到用户在不同位置的隐私要求,提出个性化隐私预算PPB算法,使用距离占比作为衡量指标,为不同位置分配个性化的隐私预算;最后,利用RN-I设计了连接点-区间混淆C-IO算法,该算法由连接点扰动和区间扰动组成,以此来混淆车辆在道路上的位置.实验结果表明,所提出的OPLPP方案优于现有的二维位置混淆方案,能够在保护用户位置隐私的前提下,提供更高的服务质量.

To address the location privacy leakage problem caused by vehicle users using location-based services in the road network,an obfuscation-based personalized location privacy protection(OPLPP)scheme is proposed.Firstly,a new concept of road network indistinguishability(RN-I)is defined based on differential privacy as a measure of the indistinguishability of locations in the road network.Secondly,considering the privacy requirements of users at different locations,a personalized privacy budget(PPB)algorithm is proposed,using distance proportion as a measure to allocate personalized privacy budgets to different locations.Finally,using RN-I to design a connection-interval obfuscation(C-IO)algorithm,which consists of connection point perturbation and interval perturbation,as a way to obfuscate the position of vehicles on the road.Experimental results show that the proposed OPLPP scheme outperforms the existing two-dimensional location obfuscation scheme and can provide a higher quality of service while protecting user location privacy.