With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on anonymity-based privacy protection, there are still several challenges far from being perfectly solved, such as the negative impact on the security of anonymity by the semantic information, which from anonymous locations and query content. To address these semantic challenges, we propose a dual privacy preservation scheme based on the architecture of multi-anonymizers in this paper. Different from existing approaches, our method enhanced location privacy by integrating location anonymity and the encrypted query. First, the query encryption method that combines improved shamir mechanism and multi-anonymizers is proposed to enhance query safety. Second, we design an anonymity method that enhances semantic location privacy through anonymous locations that satisfy personal semantic diversity and replace sensitive semantic locations. Finally, the experiment on the real dataset shows that our algorithms provide much better privacy and use than previous solutions.

Mobile traffic is experiencing tremendous growth, and this growing wave is no doubt increasing the use of radio component of mobile devices, resulting in shorter battery lifetime. In this paper, we present an Energy-Aware Download Method (EDM) based on the Markov Decision Process (MDP) to optimize the data download energy for mobile applications. Unlike the previous download schemes in literature that focus on the energy efficiency by simply delaying the download requests, which often leads to a poor user experience, our MDP model learns off-line from a set of training download workloads for different user patterns. The model is then integrated into the mobile application to deal the download request at runtime, taking into account the current battery level, LTE reference signal receiving power (RSRP), reference signal signal to noise radio (RSSNR) and task size as input of the decision process, and maximizes the reward which refers to the expected battery life and user experience. We evaluate how the EDM can be used in the context of a real file downloading application over the LTE network. We obtain, on average, 20.3%, 15% and 45% improvement respectively for energy consumption, latency, and performance of energy-delay trade off, when compared to the Android default download policy (Minimum Delay).