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Big data security challenges and strategies

Big data, a recently popular term that refers to a massive collection of very large and complex data sets, is facing serious security and privacy challenges. Due to the typical characteristics of big data, namely velocity, volume and variety associated with large-scale cloud infrastructures and the Internet of Things (IoT), traditional security and privacy mechanisms are inadequate and unable to cope with the rapid data explosion in such a complex distributed computing environment. With big data analytics being widely used by businesses and government for decision making, security risk mitigation plays an important role in big data infrastructures worldwide. Traditional security mechanisms have failed to cope with the scalability, interoperability and adaptability of contemporary technologies that are required for big data. This paper takes an exploratory initial step using first principles to address this gap in literature. Firstly, we establish the current trends in big data comprehensively by identifying eleven Vs as important dimensions of big data, which form the contributing factors having an impact on the impending security problem. Next, we map the eleven Vs to the three phases of big data life cycle in order to unearth the major security and privacy challenges of big data. Finally, the paper provides four practical strategies adapted from contemporary technologies such as data provenance, encryption and access control, data mining and blockchain, identifying their associated real implementation examples. This work would pave way for future research investigations in this important big data security arena.