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.

尽管云计算通过以外包且经济高效的方式支持数据分析而变得越来越流行，但是在将原始数据发送到云服务器时，它带来了严重的隐私问题。敏感数据具有重要价值，任何侵犯隐私的行为都可能在金钱和声誉方面造成巨大损失。因此，对于任何利益相关者都可以接受并轻松采用的任何云生态系统，隐私问题至关重要。用户的不舒服主要是由于缺乏对在Cloud环境中外包和处理的个人数据的控制。但是，缺乏云数据治理和最新的专用技术是满足不同云利益相关者隐私需求的严重障碍。所以，任何提议的Cloud平台或技术都必须考虑所需的技术措施和管理保护措施，以处理敏感数据，避免故障并受到当前和未来研究趋势的深入调查。一些研究人员进行了调查，以了解和确定云中的隐私问题。但是，他们的研究主要包括描述性文献综述。在本文中，我们提出了一种针对云隐私的整体而全面的分类法。这项研究得到了系统的文献综述的支持，该文献提供了系统的，结构化的和严格的方法，有助于理解隐私保护云策略和技术。