With the extensive development of location-based services (LBSs), the potential threats to users’ privacy information have become one of the biggest challenges. With the disclosing of users’ location data, the related privacy such as users’ activities, living address may be leaked to others. Privacy issue becomes the most concerns in LBS application scenarios. Especially for continuous LBS queries, the correlations of users’ location data make it much easier to expose their privacy information. Hence, in this paper, we propose a novel privacy preserving algorithm, namely, velocity-based dynamic cloaking algorithm (V-DCA), for continuous LBS queries. V-DCA considers users’ moving properties and trends, including velocity and acceleration similarity while performing cloaking. Moreover, V-DCA utilizes the previous cloaked sets to generate current set, and provides users’ location privacy guarantees against users tracking attack while reserve their quality of service (QoS). The main contributions of this paper are as follows: we propose a novel algorithm V-DCA for continuous LBS queries, which considers users’ velocity and acceleration similarity and is balanced between privacy preserving and QoS reserving; we define the evaluation metrics to measure its performance including privacy guarantee, cloaking time, and QoS; we evaluate the algorithm in real geographic data set environment and compare its performance with other cloaking algorithms, which shows V-DCA effectiveness in continuous LBS query application scenarios.
