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Over the length of time, there has been an exponential increase in the number of netizens throughout the world. The surge in internet users has been especially prominent in times of the Covid-19 pandemic. Consequently, there has been a rise in social data collection from social media sites and apps. In addition to it, there is machine data generated by the sensors and other industrial and medical equipment and finally, transactional data generated from transactions performed online as well as offline. The big data collected from these principal sources provides invaluable insights to various small as well as leading organizations. Hence, these organizations and researchers direct considerable attention and efforts towards the high volume, velocity and variety (referred to as the “3V”) challenges. However, due to the sheer volume of the big data, high computing power and substantial storage are needed. This feat is achieved by the use of a network of distributed systems. Since multiple systems are involved in this process, the risk of privacy breach is increased manifold. The prevention of such breaches is crucial as it may result in leakage of highly sensitive data and impose a severe threat to the privacy of individuals. The primary objective of this paper is to provide a detailed synopsis of countermeasures that can be adopted against possible data breaches at the several stages of the big data life cycle (i.e., data generation, data storage and data processing) to ensure better security of the exabytes of big data generated each day.
Varshney et al. (Wed,) studied this question.