Toward Securing Cloud-Based Data Analytics: A Discussion on Current Solutions and Open Issues

Abstract

In the last few years, organizations and business professionals have realized the value of collaborative data analytics in supporting decision-making. Where several activities are performed on online data by different stakeholders, such as cleansing, aggregation, analysis, and visualization, cloud-based data analytics has become a favored choice for business professionals due to the elasticity, availability, scalability, and pay-as-you-go features offered by cloud computing. However, large amounts of data stored on the cloud are very sensitive (e.g., innovation, financial, legal, and customers' data), and so data privacy remains one of the top concerns for many reasons; mainly those relating to legal or competition issues. In this paper, we review the security and cryptographic mechanisms which aim to make data analytics secure in a cloud environment and discuss current research challenges.

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“…However, encrypting data incurs additional storage and query processing costs -computationally expensive. Additionally, cryptographic schemes and practical systems analysed in a recent study by Moghadam et al [133] enabling the execution of queries over encrypted data (e.g., homomorphic encryption, property-preserving encryption) without decryption using analytics are both non-trivial and costly in terms of computing power and analytic processing difficulties in the fog and cloud platforms. Still, there are serious restrictions in the sharing of data between smart domains regarding privacy and security concerns where data can be reached from anywhere anytime with worldwide distributed computing environments.…”

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“…Large amounts of data stored on the cloud are very sensitive, and so data privacy remains one of the top concerns for many reasons; mainly those relating to legal or competition issues [51]. In a Gallup poll, 27% of respondents said they or someone within their household had credit card information stolen [52].…”

“…Encrypting data incurs additional storage and query processing costs -computationally expensive. Additionally, cryptographic schemes and practical systems analyzed in a recent study by Moghadam et al [51] which enable the execution of queries over encrypted data (e.g., homomorphic encryption, propertypreserving encryption) without decryption using analytics are both non-trivial and costly in terms of computing power and analytic processing difficulties. An effective approach in which insights and sanitized, cleaned and filtered data are integrated, trustworthy, and efficiently accessible is presented in Section 4 by which cyberattacks can be mitigated and bigger data can be shared in public domains to help bridge the information gaps.…”