ArticlePeerJ. Computer science2023
Scheduling algorithms for data-protection based on security-classification constraints to data-dissemination.
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.
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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.
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Authors and funding
8 authors.
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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.
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