Privacy preservation has recently received considerable attention for location-based mobile services. In this paper, we present location-dependent attack resulting from continuous and dependent location updates and propose an incremental clique-based cloaking algorithm, called ICliqueCloak, to defend against location-dependent attack. The main idea is to incrementally maintain maximal cliques for location cloaking in an un-directed graph that takes into consideration the effect of continuous location updates.
Abstract-Bicycle-sharing systems, which can provide shared bike usage services for the public, have been launched in many big cities. In bicycle-sharing systems, people can borrow and return bikes at any stations in the service region very conveniently. Therefore, bicycle-sharing systems are normally used as a shortdistance trip supplement for private vehicles as well as regular public transportation. Meanwhile, for stations located at different places in the service region, the bike usages can be quite skewed and imbalanced. Some stations have too many incoming bikes and get jammed without enough docks for upcoming bikes, while some other stations get empty quickly and lack enough bikes for people to check out. Therefore, inferring the potential destinations and arriving time of each individual trip beforehand can effectively help the service providers schedule manual bike re-dispatch in advance. In this paper, we will study the individual trip prediction problem for bicycle-sharing systems. To address the problem, we study a real-world bicycle-sharing system and analyze individuals' bike usage behaviors first. Based on the analysis results, a new trip destination prediction and trip duration inference model will be introduced. Experiments conducted on a real-world bicycle-sharing system demonstrate the effectiveness of the proposed model.
Privacy preservation has recently received considerable attention for location-based mobile services. Various location cloaking approaches have been proposed to protect the location privacy of mobile users. However, existing cloaking approaches are ill-suited for continuous queries. In view of the privacy disclosure and poor QoS (Quality of Service) under continuous query anonymization, in this paper, we propose a δp-privacy model and a δq-distortion model to balance the tradeoff between user privacy and QoS. Furthermore, two incremental utility-based cloaking algorithms -bottom-up cloaking and hybrid cloaking, are proposed to anonymize continuous queries. Experimental results validate the efficiency and effectiveness of the proposed algorithms.
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