Keywords
Data privacy
Access control
Encryption
Homomorphic encryption
Differential privacy
Federated learning
Submitted : 2026-03-25
Accepted : 2026-04-09
Published : 2026-04-24
Abstract
Cloud computing offers numerous benefits, including scalability, cost-effectiveness, and accessibility, making it an attractive solution for various organizations. However, the migration of sensitive data to cloud environments raises significant concerns regarding data privacy protection. This review paper provides a comprehensive overview of data privacy protection technologies in cloud computing. It begins by outlining the historical evolution of cloud computing and associated privacy challenges. The paper then delves into two core themes: access control mechanisms and data encryption techniques. Access control is explored in terms of attribute-based access control (ABAC), role-based access control (RBAC), and break-the-glass mechanisms. Encryption techniques are analyzed by covering homomorphic encryption, differential privacy and federated learning. The paper then offers a comparative analysis of these technologies, highlighting their strengths, weaknesses, and trade-offs in the cloud environment. Finally, the paper addresses the existing challenges and discusses future research directions, including the integration of artificial intelligence for enhanced privacy protection and the development of more robust and efficient encryption methods. This review aims to provide researchers and practitioners with a clear understanding of the current state-of-the-art in data privacy protection technologies for cloud computing and to identify potential avenues for future innovation.
References
Wang C, Wang Q, Ren K, et al., 2010, Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing, 2010 Proceedings IEEE INFOCOM, 1–9.
Itani W, Kayssi A, Chehab A, 2009, Privacy as a Service: Privacy-Aware Data Storage and Processing in Cloud Computing Architectures, 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, 711–716.
Singh N, Singh A, 2018, Data Privacy Protection Mechanisms in Cloud. Data Science and Engineering, 3(1): 24–39.
Hiremath S, Kunte S, 2017, A Novel Data Auditing Approach to Achieve Data Privacy and Data Integrity in Cloud Computing, 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), 306–310.
Sun P, 2019, Privacy Protection and Data Security in Cloud Computing: A Survey, Challenges, and Solutions. IEEE Access, 2019(7): 147420–147452.
Sharma Y, Gupta H, Khatri S, 2019, A Security Model for the Enhancement of Data Privacy in Cloud Computing, 2019 Amity International Conference on Artificial Intelligence (AICAI), 898–902.
Ghorbel A, Ghorbel M, Jmaiel M, 2017, Privacy in Cloud Computing Environments: A Survey and Research Challenges. The Journal of Supercomputing, 73(6): 2763–2800.
Ebrahimi M, Obaid A, Yeganegi K, 2020, Protecting Cloud Data Privacy against Attacks, International Conference on Innovative Computing and Cutting-edge Technologies, 421–434.
Ateeq A, Alaghbari M, Ateeq R, et al., 2024, Understanding and Addressing Data Security and Privacy Concerns in Modern Cloud Computing Systems, 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), 220–224.
Gholami A, Laure E, 2016, Security and Privacy of Sensitive Data in Cloud Computing: A Survey of Recent Developments,” arXiv, arXiv:1601.01498.
Shariati S, Ahmadzadegan M, 2015, Challenges and Security Issues in Cloud Computing from Two Perspectives: Data Security and Privacy Protection, 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), 1078–1082.
Giweli N, Shahrestani S, Cheung H, 2013, Enhancing Data Privacy and Access Anonymity in Cloud Computing, Communications of the IBIMA.