Efficient location privacy algorithm for Internet of Things (IoT) services and applications
Efficient location privacy algorithm for Internet of Things (IoT) services and applications
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DOI：
10.1016/j.jnca.2016.10.011
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发表时间：
2017-07-01
影响因子：
8.7
通讯作者：
Liao, Dan
中科院分区：
文献类型：
--
作者：
Sun, Gang;Chang, Victor;Liao, Dan
随着物联网(IoT)技术的快速发展以及智能手机和社交网络在日常生活中的无处不在的使用，基于位置的服务(LBS)已经成为一个非常重要的研究领域。虽然用户可以通过物联网享受到来自LBS的许多灵活性和便利性，但他们也可能失去隐私。拥有所有用户信息的不受信任或恶意的LBS服务器可以通过各种方式跟踪用户或向第三方发布个人数据。本文首先分析了现有的虚拟位置选择(DLS)算法--一种有效的位置隐私保护方法，并设计了一种针对DLS的攻击算法(ADLS)来测试新兴的物联网安全。为了有效地保护用户的位置隐私，综合考虑计算开销和不同用户的隐私需求，提出了一种新的虚拟位置隐私保护算法(DLP)。为了评估所提方案的效率，已经进行了大量的仿真实验。评估结果表明，ADLS算法从DLS算法中选择的虚拟位置中识别出用户的真实位置的概率很高。与DLS算法相比，我们提出的DLP算法在更低的泄露用户真实位置的概率、提高计算代价和效率(即时间、速度、精度和复杂性)方面具有明显的优势，同时保持了与DLS算法相同的隐私级别。
Location-based Services (LBS) have become a very important area for research with the rapid development of Internet of Things (IoT) technology and the ubiquitous use of smartphones and social networks in our daily lives. Although users can enjoy a lot of flexibility and conveniences from the LBS with IoT, they may also lose their privacy. Untrusted or malicious LBS servers with all users' information can track users in various ways or release personal data to third parties. In this work, we first analyze the current dummy-location selection (DLS) algorithm-an efficient location privacy preservation approach and design an attack algorithm for DLS (ADLS) for test emerging IoT security. For efficiently preserving user's location privacy, we propose a novel dummy location privacy-preserving (DLP) algorithm by considering both computational costs and various privacy requirements of different users. Extensive simulation experiments have been carried out to evaluate the efficiency of the proposed schemes. Evaluation results show that the ADLS algorithm has a high probability of identifying the user's real location out from chosen dummy locations in the DLS algorithm. Our proposed DLP algorithm has clear advantages over the DLS algorithm in term of lower probability of revealing the user's real location and improved computational cost and efficiency (i.e., time, speed, accuracy, and complexity) while preserve the same privacy level as DLS algorithm.