A Location Privacy and Query Privacy Joint Protection Scheme for POI Query in Vehicular Networks
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摘要: 在车联网中，基于位置的服务(LBS)的兴趣点(POI)查询被广泛用于车载应用中。但是，由于攻击者容易获取车辆位置、查询内容以及其它额外信息，单独对位置隐私或查询隐私进行保护很难保障车载用户的隐私安全，使得对位置隐私和查询隐私开展联合保护越发关键。为此，该文提出一种基于虚拟序列的位置隐私和查询隐私联合保护机制。首先根据POI查询的限制，分析位置隐私和查询隐私的相关性，运用欧几里得距离和关联规则算法对其建模描述，得到相关性判断模型；然后基于虚拟序列，根据影响隐私保护的因素和真实查询的相关性值，将联合保护转化为虚拟序列的选择问题，建立联合保护优化模型，得到匿名程度高且匿名区域大的匿名查询集，防止攻击者识别出真实查询。最后，实验结果表明，与现有方案相比，所提联合保护机制能抵御针对位置隐私和查询隐私的联合攻击(语义范围攻击、时间关联攻击和长期观察攻击)，能更有效地保护用户的LBS隐私。Abstract: In vehicular networks, the Point Of Interest (POI) query is widely used in Location-Based Services (LBS) for vehicle applications. However, since the attackers can easily access the location, query content, and other information, it is difficult to protect the LBS privacy of vehicle users only using location privacy protection or query privacy protection independently. Therefore, a location privacy and query privacy joint protection scheme based on dummy sequences is proposed. According to the limitations of the POI query, the correlations between location privacy and query privacy are modelled to obtain the correlation judgment model characterized by the Euclidean distance and the association rule algorithm. Moreover, based on dummy sequences, the joint protection is transformed into the dummy sequence selection according to the factors that affect user privacy and the correlation value of real query. Then a constrained multi-objective optimization model is established to obtain the query sequence with a high level of anonymity and a big cloaking region. Experimental results demonstrate that our scheme can resist joint attacks on location privacy and query privacy and protect users’ LBS privacy more efficiently than existing schemes.
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Key words:
- Location privacy /
- Location-Based Services (LBS) /
- Query privacy /
- Joint protection /
- Dummy sequences
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表 1 仿真参数
参数 符号 数值 AOR半径(km) R 3 时间段 t (8:00-9:00),(13:00-14:00),(21:00-22:00) 匿名查询集的大小 k {3,6,9,12,15,18,21} 安全距离(m) ${d_{{\text{safe}}}}$ 100 
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