KLAP for Real-World Protection of Location Privacy
KLAP for Real-World Protection of Location Privacy
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DOI：
10.1109/services.2018.00022
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
A. Shahid;N. Pissinou;S. S. Iyengar-S.;Jerry Miller;Ziqian Ding;Teresita Lemus
中科院分区：
文献类型：
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作者：
A. Shahid;N. Pissinou;S. S. Iyengar-S.;Jerry Miller;Ziqian Ding;Teresita Lemus
在基于位置的服务（LBS）中，用户需要披露他们的精确位置信息来查询服务提供商。不受信任的服务提供者可以滥用这些查询，通过时空和历史数据分析推断用户的敏感信息。为了描述LBS中现有隐私保护方法的缺点，我们提出了一种以用户为中心的混淆方法，称为KLAP，该方法基于三个基本的混淆要求：k个位置数，l个多样性和隐私区域保护。考虑到用户对不同位置的敏感性，利用实时交通信息（RTTI）， KLAP生成一个凸隐藏区域（CR）来隐藏用户的位置，使形成该区域的位置具有相似的敏感性，并且在时空域上具有抗各种推断的弹性。首次提出了一种新的CR剪枝技术，可以显著改善连续CR提交之间的延迟。我们用一个真实的数据集进行了一个实验，以显示其对零星、频繁和连续服务用例的有效性。
In Location-Based Services (LBS), users are required to disclose their precise location information to query a service provider. An untrusted service provider can abuse those queries to infer sensitive information on a user through spatio-temporal and historical data analyses. Depicting the drawbacks of existing privacy-preserving approaches in LBS, we propose a user-centric obfuscation approach, called KLAP, based on the three fundamental obfuscation requirements: k number of locations, l-diversity, and privacy area preservation. Considering user's sensitivity to different locations and utilizing Real-Time Traffic Information (RTTI), KLAP generates a convex Concealing Region (CR) to hide user's location such that the locations, forming the CR, resemble similar sensitivity and are resilient against a wide range of inferences in spatio-temporal domain. For the first time, a novel CR pruning technique is proposed to significantly improve the delay between successive CR submissions. We carry out an experiment with a real dataset to show its effectiveness for sporadic, frequent, and continuous service use cases.