The inherent dynamic features of data offer huge concerns and hurdles for researchers when it comes to storing, processing, and interpreting big data. Due to its massive, varied, and complicated data sets, it necessitates a lot of storage and processing power. Distributed systems are commonly used to meet these criteria. Big data has been implemented in the cloud environment for the goal of cost-cutting and easy management. Since cloud computing encourages virtualization, protecting one's data becomes a more difficult task. Several privacy-preserving strategies have been created at various stages of the big data life cycle to acquire additional security and privacy over the data. However, existing privacy-preserving technologies are insufficient to provide complete data protection. As a result, the goal of this article is to provide a general review of the advantages and pitfalls of several state-of-the-art privacy protection strategies for big data in the cloud. This work, in particular, demonstrates a thorough examination of existing methodologies' effectiveness and usefulness in the current situation. Various possible remedies are also proposed based on the research. The work also tackles future big data research aspects relating to privacy preservation in cloud computing contexts.
Key words: Attribute based encryption, Identity based encryption, Fully homomorphic encryption, Security, Privacy
|
