Due to tremendous use of smartphones the concern of cloud computing in mobile devices emerges, which is known as Mobile Cloud Computing (MCC). It involves the usage of mobile devices and cloud computing to perform resource intensive tasks using the internet with minimum impact on cellular resources. Nowadays, people are relying on mobile devices due to their small size and user friendly interface but due to its limited storage capacity, people can no more rely on internal RAM. Therefore, this promotes a drastic need for technology to make it possible for anyone to access their data anywhere anytime. As a result, Mobile Cloud Computing facilitates mobile users with its enticing technology by providing its on-demand and scalable services. But privacy and security are the main concern for a mobile user in the modern era. Thus, issues regarding security can be divided into cloud security and mobile network user's security, respectively. However, the primary focus of this study is to analyze how to secure the user's data in a mobile cloud. Leading to objectives, the current study presents a comprehensive analysis of existing techniques that can be considered for securing data in MCC efficiently. Moreover, this work will contribute a state-of-the-art roadmap to research and development communities for the right selection of proposed approach.

Location-Based Services (LBS) System is rapidly growing due to radio communication services with wireless mobile devices having a positioning component in it. LBS System offers location-based services by knowing the actual user position. A mobile user uses LBS to access services relevant to their locations. In order to provide Point of Interest (POI), LBS confronts numerous privacy related challenges in three different formats including Non-Trusted Third Party (NTTP), Trusted Third Party (TTP), and Mobile Peer-to-Peer (P2P). The current study emphasized the TTP based LBS system where the Location server does not provide full privacy to mobile users. In TTP based LBS system, a user's privacy is concerned with personal identity, location information, and time information. In order to accomplish privacy under these concerns, state-of-the-art existing mechanisms have been reviewed. Hence, the aim to provide a promising roadmap to research and development communities for the right selection of privacy approach has achieved by conducting a comparative survey of the TTP based approaches. Leading to these privacy attributes, the current study addressed the privacy challenge by proposing a new privacy protection model named "Improved Dummy Position" (IDP) that protects TIP (Time, Identity, and Position) attributes under TTP LBS System. In order to validate the privacy level, a comparative analysis has been conducted by implementing the proposed IDP model in the simulation tool, Riverbed Modeler academic edition. The different scenarios of changing query transferring rate evaluate the performance of the proposed model. Simulation results demonstrate that our IDP could be considered as a promising model to protect user's TIP attributes in a TTP based LBS system due to better performance and improved privacy level. Further, the proposed model extensively compared with the existing work.

The concept of Big Data become extensively popular for their vast usage in emerging technologies. Despite being complex and dynamic, big data environment has been generating the colossal amount of data which is impossible to handle from traditional data processing applications. Nowadays, the Internet of things (IoT) and social media platforms like, Facebook, Instagram, Twitter, WhatsApp, LinkedIn, and YouTube generating data in various formats. Therefore, this promotes a drastic need for technology to store and process this tremendous volume of data. This research outlines the fundamental literature required to understand the concept of big data including its nature, definitions, types, and characteristics. Additionally, the primary focus of the current study is to deal with two fundamental issues; storing an enormous amount of data and fast data processing. Leading to objectives, the paper presents Hadoop as a solution to address the problem and discussed the Hadoop Distributed File System (HDFS) and MapReduce programming framework for storage and processing in Big Data efficiently. Future research directions in this field determined based on opportunities and several emerging issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal solutions to address Big Data storage and processing problems. Moreover, this study contributes to the existing body of knowledge by comprehensively addressing the opportunities and emerging issues of Big Data.

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