A Survey on Privacy and Security in Distributed Cloud Computing: Exploring Federated Learning and Beyond
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...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10963898/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849714273615347712 |
|---|---|
| author | Ahmad Rahdari Elham Keshavarz Ehsan Nowroozi Rahim Taheri Mehrdad Hajizadeh Mohammadreza Mohammadi Sima Sinaei Thomas Bauschert |
| author_facet | Ahmad Rahdari Elham Keshavarz Ehsan Nowroozi Rahim Taheri Mehrdad Hajizadeh Mohammadreza Mohammadi Sima Sinaei Thomas Bauschert |
| author_sort | Ahmad Rahdari |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-01aa05ab89104dcaa51df6645dfb7372 |
| institution | DOAJ |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-01aa05ab89104dcaa51df6645dfb73722025-08-20T03:13:45ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0163710374410.1109/OJCOMS.2025.356003410963898A Survey on Privacy and Security in Distributed Cloud Computing: Exploring Federated Learning and BeyondAhmad Rahdari0https://orcid.org/0000-0002-6368-1435Elham Keshavarz1https://orcid.org/0009-0009-8194-0312Ehsan Nowroozi2https://orcid.org/0000-0002-5714-8378Rahim Taheri3https://orcid.org/0000-0002-4078-3105Mehrdad Hajizadeh4https://orcid.org/0009-0008-3375-0912Mohammadreza Mohammadi5Sima Sinaei6https://orcid.org/0000-0001-5951-9374Thomas Bauschert7School of Electrical and Computer Engineering, Shiraz University, Shiraz, IranDepartment of Computer Engineering, Pishtazan Institute of Higher Education, Shiraz, IranCentre for Sustainable Cyber Security, University of Greenwich, London, U.K.PAIDS Reserach Centre, School of Computing, University of Portsmouth, Portsmouth, U.K.Chair of Communication Networks, Technische Universität Chemnitz, Chemnitz, GermanyDepartment Digital Systems, Smart Automation and Cyber Resilience (SACR), Gothenburg, SwedenDepartment Digital Systems, Smart Automation and Cyber Resilience (SACR), Gothenburg, SwedenChair of Communication Networks, Technische Universität Chemnitz, Chemnitz, GermanyThe 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.https://ieeexplore.ieee.org/document/10963898/Distributed cloud computingedge computingprivacy-preserving computingfederated learningmulti-party computationdifferential privacy |
| spellingShingle | Ahmad Rahdari Elham Keshavarz Ehsan Nowroozi Rahim Taheri Mehrdad Hajizadeh Mohammadreza Mohammadi Sima Sinaei Thomas Bauschert A Survey on Privacy and Security in Distributed Cloud Computing: Exploring Federated Learning and Beyond IEEE Open Journal of the Communications Society Distributed cloud computing edge computing privacy-preserving computing federated learning multi-party computation differential privacy |
| title | A Survey on Privacy and Security in Distributed Cloud Computing: Exploring Federated Learning and Beyond |
| title_full | A Survey on Privacy and Security in Distributed Cloud Computing: Exploring Federated Learning and Beyond |
| title_fullStr | A Survey on Privacy and Security in Distributed Cloud Computing: Exploring Federated Learning and Beyond |
| title_full_unstemmed | A Survey on Privacy and Security in Distributed Cloud Computing: Exploring Federated Learning and Beyond |
| title_short | A Survey on Privacy and Security in Distributed Cloud Computing: Exploring Federated Learning and Beyond |
| title_sort | survey on privacy and security in distributed cloud computing exploring federated learning and beyond |
| topic | Distributed cloud computing edge computing privacy-preserving computing federated learning multi-party computation differential privacy |
| url | https://ieeexplore.ieee.org/document/10963898/ |
| work_keys_str_mv | AT ahmadrahdari asurveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT elhamkeshavarz asurveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT ehsannowroozi asurveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT rahimtaheri asurveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT mehrdadhajizadeh asurveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT mohammadrezamohammadi asurveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT simasinaei asurveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT thomasbauschert asurveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT ahmadrahdari surveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT elhamkeshavarz surveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT ehsannowroozi surveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT rahimtaheri surveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT mehrdadhajizadeh surveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT mohammadrezamohammadi surveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT simasinaei surveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond AT thomasbauschert surveyonprivacyandsecurityindistributedcloudcomputingexploringfederatedlearningandbeyond |