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REVIEW 2 major objections 168 references

Cloud Computing Review: A Decade of Research

T0 review · 2 major / 0 minor · reviewed 2026-06-30 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A scientometric analysis of cloud computing publications from 2014 to 2023 maps collaboration patterns and research clusters.

desk verdict Standard CiteSpace mapping of cloud computing papers from 2014-2023 with no new methods or validation details. read the letter →

arxiv 2605.24499 v1 pith:AO4WVF42 submitted 2026-05-23 cs.GT

Smruti Rekha Swain This is my paper
classification cs.GT
keywords cloudcomputingscientometricanalysisCiteSpacebibliometricreviewresearchtrendscollaborationnetworksco-citation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper applies scientometric techniques to examine the evolution of cloud computing research over a decade. It retrieves papers from the Web of Science Core database and uses the CiteSpace tool to build networks showing collaborations among authors, institutions, and countries. The analysis also examines journal co-citations and document co-citation networks to identify core topics and the overall knowledge structure. This macroscopic view is intended to highlight patterns and trends that can guide future research activities and collaborations.

What carries the argument

CiteSpace visual analysis applied to Web of Science data to generate collaboration networks and document co-citation networks with clustering.

What would settle it

A check that finds many high-impact cloud computing papers or major subfields missing from the Web of Science results, or that the generated topic clusters do not match independent expert assessments of the field's main areas.

Watch

Extended reading notes

Core claim

The central claim is that constructing collaboration networks among authors, institutions, and countries, along with journal and document co-citation networks, reveals the underlying research topics and knowledge structure of cloud computing, thereby enhancing understanding of patterns, trends, and critical factors in the field.

Load-bearing premise

Papers retrieved via the chosen query from the Web of Science database and processed through CiteSpace represent the overall structure of cloud computing research without major selection bias or coverage gaps.

Editorial extensions

If this is right

  • The analysis identifies the most prolific authors, institutions, and countries contributing to cloud computing research.
  • Journal co-citation analysis shows the distribution of core journals in the field.
  • Document co-citation networks and clustering uncover the main research topics and knowledge structure.
  • The resulting overview supplies a basis for directing research activities and fostering collaborations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The identified clusters could help researchers locate active sub-areas or gaps within cloud computing.
  • Collaboration network data might highlight potential cross-institutional or international partnerships not yet fully realized.
  • Repeating the analysis with data from additional databases could test whether the observed patterns persist.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 0 minor

Summary. The paper performs a scientometric mapping of cloud computing research published 2014–2023, retrieving records from the Web of Science Core Collection and applying the CiteSpace tool to generate author, institution, and country collaboration networks, journal co-citation distributions, document co-citation networks, and topic clusters. The central claim is that these visualizations reveal prolific contributors, underlying research topics, and the knowledge structure of the field, thereby providing a foundation for guiding future research and collaboration.

Significance. A transparent and reproducible scientometric overview of this scale could usefully synthesize a decade of interdisciplinary cloud-computing literature and highlight collaboration patterns. The work employs standard, off-the-shelf tools on a well-known database, but the absence of methodological specifics prevents assessment of whether the reported networks and clusters are robust or merely artifacts of unstated parameter choices and database coverage.

major comments (2)
  1. [Methods] Methods (or equivalent section describing data retrieval): the search string, Boolean operators, and exact query submitted to Web of Science are never stated, so it is impossible to determine the scope of the corpus or to replicate the selection step that underpins every subsequent network and cluster.
  2. [Methods] Methods (CiteSpace analysis): no values are supplied for time slicing, node-type thresholds, link-strength cut-offs, or clustering parameters (e.g., silhouette score, modularity), rendering the reported collaboration networks and co-citation clusters non-reproducible and preventing evaluation of sensitivity to these choices.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed review and for emphasizing the importance of methodological transparency and reproducibility in our scientometric analysis of cloud computing research. We agree that the current manuscript lacks sufficient detail on data retrieval and CiteSpace parameters, and we will revise accordingly to address these points.

read point-by-point responses

  1. Referee: [Methods] Methods (or equivalent section describing data retrieval): the search string, Boolean operators, and exact query submitted to Web of Science are never stated, so it is impossible to determine the scope of the corpus or to replicate the selection step that underpins every subsequent network and cluster.

    Authors: We acknowledge the omission. The revised manuscript will include the complete search string, Boolean operators, and the exact query submitted to the Web of Science Core Collection, enabling full replication of the corpus selection. revision: yes

  2. Referee: [Methods] Methods (CiteSpace analysis): no values are supplied for time slicing, node-type thresholds, link-strength cut-offs, or clustering parameters (e.g., silhouette score, modularity), rendering the reported collaboration networks and co-citation clusters non-reproducible and preventing evaluation of sensitivity to these choices.

    Authors: We agree that these parameters must be specified. The revision will report the time slicing (yearly intervals 2014–2023), node-type thresholds, link-strength cut-offs, and clustering parameters including modularity and silhouette scores used to generate the networks and clusters. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity

full rationale

This is a descriptive scientometric mapping paper that retrieves papers from the external Web of Science Core database (2014-2023) and applies the off-the-shelf CiteSpace tool to generate collaboration networks, co-citation networks, and clusters. No equations, fitted parameters, predictions, or derivations appear; the reported outputs are direct products of the chosen external data source and standard software. No self-citations are load-bearing, no ansatzes are smuggled, and no results reduce by construction to author-defined quantities. The central claim is a modest descriptive statement about patterns within the sampled corpus.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The central claim rests on the domain assumption that scientometric visualization of database records reveals the true macroscopic knowledge structure of the field.

assumptions (1)
  • domain assumption Scientometric techniques applied to Web of Science records can empirically examine the evolution and current state of cloud computing research from a macroscopic perspective.
    Stated directly in the abstract as the justification for the entire study.

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Cite this review

Pith. "Pith review of Cloud Computing Review: A Decade of Research." pith.science (2026). https://pith.science/paper/AO4WVF42

@misc{pith2026260524499,
  author       = {Pith},
  title        = {Pith review of: Cloud Computing Review: A Decade of Research},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AO4WVF42}},
  note         = {Machine review of arXiv:2605.24499}
}
read the original abstract

The popularity and rapid development of Cloud Computing in recent years has led to a vast number of publications capturing the accumulated knowledge in this field. Due to the interdisciplinary nature and significant relevance of cloud computing research, it has become increasingly challenging to comprehend the overall structure and progress of this field without employing analytical methods. While the evaluation of scientific research has a long tradition in many fields, we have identified a lack of comprehensive scientometric studies specifically focused on cloud computing. This study applies scientometric techniques to empirically examine cloud computing research evolution and current state from a macroscopic perspective. We employed the CiteSpace tool for visual analysis, exploring topics related to cloud computing by retrieving papers published between 2014 and 2023 from the Web of Science Core database. Our approach involved constructing collaboration networks among authors, institutions, and countries to pinpoint the most prolific contributors in each category. Through the analysis of core journal distributions via journal co-citations, document co-citation networks, and clustering analysis, we uncovered the underlying research topics and knowledge structure. The results of this study enhance our understanding of patterns, trends, and other critical factors, offering a foundation for guiding research activities, sharing knowledge, and fostering collaboration in the field of cloud computing research.

Figures

Figures reproduced from arXiv: 2605.24499 by the authors.

Figure 1
Figure 1. Research Methodology comprehensive scientometric study that empirically investigates publications of cloud computing, encompassing data from the Web of Science database spanning from 2014 to 2023. In total, we analyze 69,142 publications, marking the first-ever scientometric study to scrutinize such a vast number of peer-reviewed publications. This study offers comprehensive insights into publishing patterns, such a… view at source ↗
Figure 2
Figure 2. Prisma Flow Diagram for Record Selection [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Document Co-citation Analysis 5 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Overall Publication Growth 3 Visualization Scientometric Analysis In this section, the authors perform a scientometric and systematic analysis of the literature in the field of cloud computing. The objective is to uncover bibliometric aspects and extract knowledge from…
Figure 5
Figure 5. Figure 5: Domainwise overall publication growth in CC [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Overall country collaboration network 3.2 Co-Citation Analysis 3.2.1 Documentation co-citation Analysis Documents serve as repositories of critical knowledge. When two or more documents are cited simultaneously by one or more subsequent documents, they share a co-citat…
Figure 7
Figure 7. Figure 7: Document co-citation network [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: Author co-citation network [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: Journal Co-citation Network 3.3 Co-Word Analysis Keywords serve as effective tools for abstractly representing and categorizing the content of a scientific article. From a meta-perspective, they form the foundation for analyzing the key topics and aspects that define a…
Figure 10
Figure 10. Figure 10: Keyword co-citation Analysis 13 [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 11
Figure 11. Figure 11: Top 25 keywords with citation bursts. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_11.png]

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    Fedmup: Federated learning driven malicious user prediction model for secure data distribution in cloud environments

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    A. Liberati, D. G. Altman, J. Tetzlaff, C. Mulrow, P. C. Gøtzsche, J. P. Ioannidis, M. Clarke, P. J. Devereaux, J. Kleijnen, and D. Moher, “The prisma statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation a...

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    R. Gupta and A. K. Singh, “A privacy-preserving model for cloud data storage through fog computing,”Interna- tional Journal of Computer Aided Engineering and Technology, vol. 17, no. 3, pp. 348–359, 2022

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    Hedcm: Human emotions detection and classification model from speech using cnn,

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    Emotional information-based hybrid recommen- dation system,

    M. Sharma, R. Mittal, A. Bharati, D. Saxena, and A. K. Singh, “Emotional information-based hybrid recommen- dation system,” inSoft Computing for Problem Solving: Proceedings of the SocProS 2022. Springer, 2023, pp. 249–267

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    Efficient resource management in cloud environment,

    S. R. Swain, A. K. Singh, and C. N. Lee, “Efficient resource management in cloud environment,”arXiv preprint arXiv:2207.12085, 2022

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    Auxiliary informatics system: an advancement towards a smart home environment,

    I. Gupta, P. K. Yadav, S. Pareek, S. Shakeel, and A. K. Singh, “Auxiliary informatics system: an advancement towards a smart home environment,” 2022

    2022

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    Pca-rf: An efficient parkinson’s disease prediction model based on random forest classification,

    I. Gupta, V . Sharma, S. Kaur, and A. K. Singh, “Pca-rf: An efficient parkinson’s disease prediction model based on random forest classification,”arXiv preprint arXiv:2203.11287, 2022

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    An efficient design of scalable reversible multiplier with testability,

    H. M. Gaur, A. K. Singh, and U. Ghanekar, “An efficient design of scalable reversible multiplier with testability,” Journal of Circuits, Systems and Computers, p. 2250179, 2022

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    Mlrm: A multiple linear regression based model for average temperature prediction of a day,

    I. Gupta, H. Mittal, D. Rikhari, and A. K. Singh, “Mlrm: A multiple linear regression based model for average temperature prediction of a day,”arXiv preprint arXiv:2203.05835, 2022

    2022

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    Hisa-smfm: Historical and sentiment analysis based stock market forecasting model,

    I. Gupta, T. K. Madan, S. Singh, and A. K. Singh, “Hisa-smfm: Historical and sentiment analysis based stock market forecasting model,”arXiv preprint arXiv:2203.08143, 2022

    2022

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    Tidf-dlpm: Term and inverse document frequency based data leakage prevention model,

    I. Gupta, S. Mittal, A. Tiwari, P. Agarwal, and A. K. Singh, “Tidf-dlpm: Term and inverse document frequency based data leakage prevention model,”arXiv preprint arXiv:2203.05367, 2022

    2022

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    An intelligent traffic entropy learning-based load management model for cloud networks,

    D. Saxena and A. K. Singh, “An intelligent traffic entropy learning-based load management model for cloud networks,”IEEE Networking Letters, vol. 4, no. 2, pp. 59–63, 2022

    2022

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    A holistic view on data protection for sharing, communicating, and computing environments: Taxonomy and future directions,

    I. Gupta and A. K. Singh, “A holistic view on data protection for sharing, communicating, and computing environments: Taxonomy and future directions,”arXiv preprint arXiv:2202.11965, 2022

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    Secureiiot environment: Federated learning empowered approach for securing iiot from data breach,

    A. Makkar, T. W. Kim, A. K. Singh, J. Kang, and J. H. Park, “Secureiiot environment: Federated learning empowered approach for securing iiot from data breach,”IEEE Transactions on Industrial Informatics, 2022

    2022

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    Maci: Malicious api call identifier model to secure the host platform,

    A. Acharya, H. Prasad, V . Kumar, I. Gupta, and A. K. Singh, “Maci: Malicious api call identifier model to secure the host platform,” inProceedings of the Seventh International Conference on Mathematics and Computing. Springer, 2022, pp. 309–320

    2022

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    Lstm and nlp based forecasting model for stock market analysis,

    R. Patel, V . Choudhary, D. Saxena, and A. K. Singh, “Lstm and nlp based forecasting model for stock market analysis,” in2021 First International Conference on Advances in Computing and Future Communication Technologies (ICACFCT). IEEE, 2021, pp. 52–57

    2021

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    Communication cost aware resource efficient load balancing (care-lb) framework for cloud datacenter,

    D. Saxena and A. K. Singh, “Communication cost aware resource efficient load balancing (care-lb) framework for cloud datacenter,”Recent Advances in Computer Science and Communications, vol. 12, pp. 1–00, 2020

    2020

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    Performance evaluation of metaheuristics algorithms for workload prediction in cloud environment,

    J. Kumar and A. K. Singh, “Performance evaluation of metaheuristics algorithms for workload prediction in cloud environment,”Applied Soft Computing, vol. 113, p. 107895, 2021

    2021

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    A survey and comparative study on multi-cloud architectures: emerging issues and challenges for cloud federation,

    D. Saxena, R. Gupta, and A. K. Singh, “A survey and comparative study on multi-cloud architectures: emerging issues and challenges for cloud federation,”arXiv preprint arXiv:2108.12831, 2021

    2021

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    A study on machine learning approaches for player performance and match results prediction,

    H. Mittal, D. Rikhari, J. Kumar, and A. K. Singh, “A study on machine learning approaches for player performance and match results prediction,”arXiv preprint arXiv:2108.10125, 2021

    2021

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    Credit card fraud detection using machine learning: A study,

    P. Tiwari, S. Mehta, N. Sakhuja, J. Kumar, and A. K. Singh, “Credit card fraud detection using machine learning: A study,”arXiv preprint arXiv:2108.10005, 2021

    2021

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    Testable designs of toffoli fredkin reversible circuits,

    H. M. Gaur, A. K. Singh, and U. Ghanekar, “Testable designs of toffoli fredkin reversible circuits,”arXiv preprint arXiv:2108.07448, 2021

    2021

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    Workload forecasting and resource management models based on machine learning for cloud computing environments,

    D. Saxena and A. K. Singh, “Workload forecasting and resource management models based on machine learning for cloud computing environments,”arXiv preprint arXiv:2106.15112, 2021

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    A comparative study on cipher text policy attribute based encryption schemes,

    B. Pradhan, B. Singh, A. Bhoria, and A. K. Singh, “A comparative study on cipher text policy attribute based encryption schemes,” 2021

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    Machine learning techniques for plant disease detection,

    D. Varshney, B. Babukhanwala, J. Khan, D. Saxena, and A. kumar Singh, “Machine learning techniques for plant disease detection,” in2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2021, pp. 1574–1581

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    Review of stock prediction using machine learning techniques,

    R. Patel, V . Choudhary, D. Saxena, and A. K. Singh, “Review of stock prediction using machine learning techniques,” in2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2021, pp. 840–846. 21 APREPRINT- MAY26, 2026

    2021

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    A review of fake news detection methods using machine learning,

    M. Choudhary, S. Jha, D. Saxena, A. K. Singhet al., “A review of fake news detection methods using machine learning,” in2021 2nd International Conference for Emerging Technology (INCET). IEEE, 2021, pp. 1–5

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    J. Kumar, A. K. Singh, and R. Buyya, “Self directed learning based workload forecasting model for cloud resource management,”Information Sciences, vol. 543, pp. 345–366, 2021

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    Host platform security and mobile agent classification: A systematic study,

    A. Acharya, H. Prasad, V . Kumar, I. Gupta, and A. K. Singh, “Host platform security and mobile agent classification: A systematic study,” inComputer Networks and Inventive Communication Technologies. Springer, 2021, pp. 1001–1010

    2021

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    Real-time human locator and advance home security appliances,

    A. Kesharwani, A. Nag, A. Tiwari, I. Gupta, B. Sharma, and A. K. Singh, “Real-time human locator and advance home security appliances,” inEvolutionary Computing and Mobile Sustainable Networks. Springer, 2021, pp. 37–49

    2021

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    A lightweight effective randomized caesar cipher algorithm for security of data,

    V . Sharma, S. Jalwa, A. R. Siddiqi, I. Gupta, and A. K. Singh, “A lightweight effective randomized caesar cipher algorithm for security of data,” inEvolutionary Computing and Mobile Sustainable Networks. Springer, 2021, pp. 411–419

    2021

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    Hybrid method in identifying the fraud detection in the credit card,

    P. Tiwari, S. Mehta, N. Sakhuja, I. Gupta, and A. K. Singh, “Hybrid method in identifying the fraud detection in the credit card,” inEvolutionary Computing and Mobile Sustainable Networks. Springer, 2021, pp. 27–35

    2021

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    Comprehensive and comparative analysis of different files using cp-abe,

    S. Jalwa, V . Sharma, A. R. Siddiqi, I. Gupta, and A. K. Singh, “Comprehensive and comparative analysis of different files using cp-abe,” inAdvances in Communication and Computational Technology. Springer, 2021, pp. 189–198

    2021

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    Online information leaker identification scheme for secure data sharing,

    A. K. Singh and I. Gupta, “Online information leaker identification scheme for secure data sharing,”Multimedia Tools and Applications, vol. 79, no. 41, pp. 31 165–31 182, 2020

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    Decomposition based cloud resource demand prediction using extreme learning machines,

    J. Kumar and A. K. Singh, “Decomposition based cloud resource demand prediction using extreme learning machines,”Journal of Network and Systems Management, vol. 28, no. 4, pp. 1775–1793, 2020

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    Seli: statistical evaluation based leaker identification stochastic scheme for secure data sharing,

    I. Gupta and A. K. Singh, “Seli: statistical evaluation based leaker identification stochastic scheme for secure data sharing,”IET Communications, vol. 14, no. 20, pp. 3607–3618, 2020

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    An integrated approach for data leaker detection in cloud environment

    ——, “An integrated approach for data leaker detection in cloud environment.”Journal of Information Science & Engineering, vol. 36, no. 5, 2020

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    Guim-smd: guilty user identification model using summation matrix-based distribution,

    ——, “Guim-smd: guilty user identification model using summation matrix-based distribution,”IET Information Security, vol. 14, no. 6, pp. 773–782, 2020

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    G. S. Hura, A. K. Singh, and L. S. Hoe,Advances in Communication and Computational Technology: Select Proceedings of ICACCT 2019. Springer, 2020

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    A review on data privacy using attribute-based encryption,

    D. Deepika, R. Malik, S. Kumar, R. Gupta, and A. K. Singh, “A review on data privacy using attribute-based encryption,” inProceedings of the International Conference on Innovative Computing & Communications (ICICC), 2020

    2020

  147. [157]

    A review on emotion detection and classification using speech,

    A. Tripathi, U. Singh, G. Bansal, R. Gupta, and A. K. Singh, “A review on emotion detection and classification using speech,” inProceedings of the International Conference on Innovative Computing & Communications (ICICC), 2020

    2020

  148. [158]

    A survey on privacy-preserving outsourced data on cloud with multiple data providers,

    A. S. Chauhan, D. Rani, A. Kumar, R. Gupta, and A. K. Singh, “A survey on privacy-preserving outsourced data on cloud with multiple data providers,” inProceedings of the International Conference on Innovative Computing & Communications (ICICC), 2020

    2020

  149. [159]

    A framework for malicious agent detection in cloud computing environment,

    I. Gupta and A. Singh, “A framework for malicious agent detection in cloud computing environment,”Int J Adv Sci Technol (IJAST), vol. 135, pp. 49–62, 2020

    2020

  150. [160]

    Layer-based privacy and security architecture for cloud data sharing,

    I. Gupta, N. Singh, and A. K. Singh, “Layer-based privacy and security architecture for cloud data sharing,” Journal of Communications Software and Systems, vol. 15, no. 2, pp. 173–185, 2019

    2019

  151. [161]

    Authenticating cryptography over network in data,

    P. Agarwal, S. Mittal, A. Tiwari, I. Gupta, A. K. Singh, and B. Sharma, “Authenticating cryptography over network in data,” in2019 International Conference on Intelligent Computing and Control Systems (ICCS). IEEE, 2019, pp. 632–636

    2019

  152. [162]

    Advancements and security issues of iot & cyber physical systems,

    P. K. Yadav, S. Pareek, S. Shakeel, J. Kumar, and A. K. Singh, “Advancements and security issues of iot & cyber physical systems,” in2019 International Conference on Intelligent Computing and Control Systems (ICCS). IEEE, 2019, pp. 940–945

    2019

  153. [163]

    A confidentiality preserving data leaker detection model for secure sharing of cloud data using integrated techniques,

    I. Gupta and A. K. Singh, “A confidentiality preserving data leaker detection model for secure sharing of cloud data using integrated techniques,” in2019 7th International Conference on Smart Computing & Communications (ICSCC). IEEE, 2019, pp. 1–5. 22 APREPRINT- MAY26, 2026

    2019

  154. [164]

    Sql-injection vulnerabilities resolving using valid security tool in cloud

    N. Singh and A. K. Singh, “Sql-injection vulnerabilities resolving using valid security tool in cloud.”Pertanika Journal of Science & Technology, vol. 27, no. 1, 2019

    2019

  155. [165]

    A probabilistic model for finding an optimal host framework and load distribution in cloud environment,

    S. Chhabra and A. K. Singh, “A probabilistic model for finding an optimal host framework and load distribution in cloud environment,”Procedia Computer Science, vol. 125, pp. 683–690, 2018

    2018

  156. [166]

    A probabilistic approach for guilty agent detection using bigraph after distribution of sample data,

    I. Gupta and A. K. Singh, “A probabilistic approach for guilty agent detection using bigraph after distribution of sample data,”Procedia Computer Science, vol. 125, pp. 662–668, 2018

    2018

  157. [167]

    Oph-lb: Optimal physical host for load balancing in cloud environment

    S. Chhabra and A. K. Singh, “Oph-lb: Optimal physical host for load balancing in cloud environment.”Pertanika Journal of Science & Technology, vol. 26, no. 3, 2018

    2018

  158. [168]

    Compendium of data security in cloud storage by applying hybridization of encryption algorithm,

    I. Gupta, D. Gurnani, N. Gupta, C. Singla, P. Thakral, and A. K. Singh, “Compendium of data security in cloud storage by applying hybridization of encryption algorithm,” 2022

    2022

  159. [169]

    Data privacy protection mechanisms in cloud,

    N. Singh and A. K. Singh, “Data privacy protection mechanisms in cloud,”Data Science and Engineering, vol. 3, no. 1, pp. 24–39, 2018

    2018

  160. [170]

    Data leakage prevention: e-mail protection via gateway,

    K. Kaur, I. Gupta, and A. K. Singh, “Data leakage prevention: e-mail protection via gateway,” inJournal of Physics: Conference Series, vol. 933. IOP Publishing, 2017, p. 012013. 23

    2017

Pith tools

Reviewed June 30, 2026 · model on record in the stance chip above.