With a growing rate of mobile usage in the current networking era, mobile devices have been adopted widely. With mobile proliferation, location awareness is gaining huge insights in mobile and communication technologies for which, information subsystem has to be maintained on geo-located systems. Location Dependent Services (LDS) fetch relevant information from the geo-located information subsystems and deliver it to the user based on their location preferences and choices. Despite having the desirable features and services, LDS does not provide satisfactory protection to the user’s geographic location. Preservation of location has become one of the utmost concerns in mobile network communication. In this paper, we will go through the security & privacy challenges for LDS in mobile networks. Precisely, this paper covers the cryptographic approaches and technologies to enhance privacy to promote location privacy in mobile network communication. The different approaches covered in the review are first compared and then the further research areas have been identified as well as concluded.

Machine learning is the branch of artificial intelligence which makes the computers capable enough to learn from the input data using statistical models and algorithms to imitate the human learning process, gradually becoming more accurate at outcome prediction without being explicitly programmed to do so. Once the algorithm learns what task it has to do with the data, it works on it automatically. Deep learning, also referred to as deep neural network or deep neural learning, on the other hand, is the branch of machine learning, which is just a neural network with many layers to optimize and upgrade for accuracy.

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