Abstract
Although Cloud computing is gaining popularity by supporting data analysis in an outsourced and cost-effective way, it brings serious privacy issues when sending the original data to Cloud servers. Sensitive data have a significant value, and any infringement of privacy can cause great loss in terms of money and reputation. Thus, for any Cloud ecosystem to be accepted and easily adopted by different stakeholders, privacy concerns are of utmost importance. Users’ discomfort is mainly due to the lack of control over their personal’s data outsourced and processed on the Cloud environment. However, the lack of Cloud data governance and the absence of up-to-date dedicated technologies represent serious barriers to satisfy different Cloud stakeholders’ privacy needs. Consequently, any proposed Cloud platform or technology must consider required technical measures and managerial safeguards to handle sensitive data, to avoid breakdowns, and be intensely investigated by current and future research trends. Several researchers have conducted surveys to understand and target privacy issues in the Cloud. However, their research consists mostly of descriptive literature reviews. In this paper, we propose a holistic and comprehensive taxonomy for Cloud privacy. This study is supported by the results of a systematic literature review, which provides a methodical, structured, and rigorous approach facilitating the understanding of privacy-preserving Cloud strategies and techniques. The study’s objective is to offer a credible intellectual guide for upcoming researchers in Cloud privacy and identify active privacy research areas to make the most impact.
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Akremi, A., Rouached, M. A comprehensive and holistic knowledge model for cloud privacy protection. J Supercomput 77, 7956–7988 (2021). https://doi.org/10.1007/s11227-020-03594-3
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DOI: https://doi.org/10.1007/s11227-020-03594-3