Evidence map›Paper›PMID 38077537›Full record

ArticlePeerJ. Computer science2023

Scheduling algorithms for data-protection based on security-classification constraints to data-dissemination.

Mohammad Mahmood Otoom, Mahdi Jemmali, Wael M Khedr, Akram Y Sarhan, Imen Achour, Ibrahim Alsaduni, Abdullah Bajahzar, Mohamed Nazih Omri

Abstract read
In one paragraph

Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Mohammad Mahmood OtoomDepartment of Computer Science and Information, College of Science, Majmaah University, Majmaah, Saudi Arabia.
Mahdi JemmaliDepartment of Computer Science and Information, College of Science, Majmaah University, Majmaah, Saudi Arabia.
Wael M KhedrDepartment of Computer Science and Information, College of Science, Majmaah University, Majmaah, Saudi Arabia.
Akram Y SarhanDepartment of Information Technology, College of Computing and Information Technology at Khulis, University of Jeddah, Jeddah, Saudi Arabia.
Imen AchourDepartment of Computer Science and Information, College of Science, Majmaah University, Majmaah, Saudi Arabia.
Ibrahim AlsaduniDepartment of Electrical Engineering, College of Engineering, Majmaah University, Majmaah, Saudi Arabia.
Abdullah BajahzarDepartment of Computer Science and Information, College of Science, Majmaah University, Majmaah, Saudi Arabia.
Mohamed Nazih OmriMars Laboratory, University of Sousse, Sousse, Tunisia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Communication networks have played a vital role in changing people's life. However, the rapid advancement in digital technologies has presented many drawbacks of the current inter-networking technology. Data leakages severely threaten information privacy and security and can jeopardize individual and public life. This research investigates the creation of a private network model that can decrease the number of data leakages. A two-router private network model is designed. This model uses two routers to manage the classification level of the transmitting network packets. In addition, various algorithmic techniques are proposed. These techniques solve a scheduling problem. This problem is to schedule packets through routers under a security classification level constraint. This constraint is the non-permission of the transmission of two packets that belongs to the same security classification level. These techniques are the dispatching rule and grouping method. The studied problem is an NP-hard. Eight algorithms are proposed to minimize the total transmission time. A comparison between the proposed algorithms and those in the literature is discussed to show the performance of the proposed scheme through experimentation. Four classes of instances are generated. For these classes, the experimental results show that the best-proposed algorithm is the best-classification groups' algorithm in 89.1% of cases and an average gap of 0.001. In addition, a benchmark of instances is used based on a real dataset. This real dataset shows that the best-proposed algorithm is the best-classification groups' algorithm in 88.6% of cases and an average gap of less than 0.001.

Indexed as

AlgorithmsBig dataCybersecurityData-disseminationNetworksPacket-transmission

Identifiers

PMID38077537
PMCPMC10702719

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.