Rahdari, Ahmad, Keshavarz, Elham, Nowroozi, Ehsan ORCID: https://orcid.org/0000-0002-5714-8378, Taheri, Rahim, Hajizadeh, Mehrdad, Mohammadi, Mohammadreza, Sinaei, Sima and Bauschert, Thomas (2025) A survey on privacy and security in distributed cloud computing: exploring federated learning and beyond. IEEE Open Journal of the Communications Society. ISSN 2644-125X (Online) (doi:10.1109/OJCOMS.2025.3560034)

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Abstract

The increasing need to process large, high-dimensional datasets and the substantial computational power required have made the use of distributed cloud servers essential. These servers provide cost-effective solutions that make storage and computing accessible to ordinary users. However, they might face significant vulnerabilities, including data leakage, metadata spoofing, insecure programming interfaces, malicious insiders, and denial of service. To gain public trust in distributed computing, addressing concerns related to privacy and security while ensuring high performance and efficiency is crucial. Multiparty computation, differential privacy, trusted execution environments, and federated learning are the four major approaches developed to address these issues. This survey paper reviews and compares these four approaches based on a structured framework, by highlighting recent top-tier research papers published in prestigious journals and conferences. Particular attention is given to progress in federated learning, which trains a model across multiple devices without sharing the actual data, keeping data private and secure. The survey also highlights federated learning techniques, including secure federated learning, by detecting malicious updates and privacy-preserving federated learning via data encryption, data perturbation, and anonymization, as new paradigms for building responsible computing systems. Finally, the survey discusses future research directions for connecting academic innovations with real-world industrial applications.

Item Type:	Article
Uncontrolled Keywords:	distributed cloud computing, edge computing, privacy-preserving computing, federated Learning; Multi-Party Computation; Differential Privacy; Trusted Execution Environments
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HE Transportation and Communications Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	16 Apr 2025 14:11
URI:	http://gala.gre.ac.uk/id/eprint/50212

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