Biblio

Location privacy has become a significant challenge of big data. Particularly, by the advantage of big data handling tools availability, huge location data can be managed and processed easily by an adversary to obtain user private information from Location-Based Services (LBS). So far, many methods have been proposed to preserve user location privacy for these services. Among them, dummy-based methods have various advantages in terms of implementation and low computation costs. However, they suffer from the spatiotemporal correlation issue when users submit consecutive requests. To solve this problem, a practical hybrid location privacy protection scheme is presented in this paper. The proposed method filters out the correlated fake location data (dummies) before submissions. Therefore, the adversary can not identify the user's real location. Evaluations and experiments show that our proposed filtering technique significantly improves the performance of existing dummy-based methods and enables them to effectively protect the user's location privacy in the environment of big data.

Infrastructure-based Vehicular Networks can be applied in different social contexts, such as health care, transportation and entertainment. They can easily take advantage of the benefices provided by wireless mesh networks (WMNs) to mobility, since WMNs essentially support technological convergence and resilience, required for the effective operation of services and applications. However, infrastructure-based vehicular networks are prone to attacks such as ARP packets flooding that compromise mobility management and users' network access. Hence, this work proposes MIRF, a secure mobility scheme based on reputation and filtering to mitigate flooding attacks on mobility management. The efficiency of the MIRF scheme has been evaluated by simulations considering urban scenarios with and without attacks. Analyses show that it significantly improves the packet delivery ratio in scenarios with attacks, mitigating their intentional negative effects, as the reduction of malicious ARP requests. Furthermore, improvements have been observed in the number of handoffs on scenarios under attacks, being faster than scenarios without the scheme.