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

Author:

Otoom Mohammad Mahmood1,Jemmali Mahdi123,Khedr Wael M.14,Sarhan Akram Y.5,Achour Imen1,Alsaduni Ibrahim6,Bajahzar Abdullah1,Omri Mohamed Nazih2

Affiliation:

1. Department of Computer Science and Information, College of Science, Majmaah University, Majmaah, Saudi Arabia

2. Mars Laboratory, University of Sousse, Sousse, Tunisia

3. Department of Computer Science, Higher Institute of Computer Science and Mathematics, University of Monastir, Monastir, Tunisia

4. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt

5. Department of Information Technology, College of Computing and Information Technology at Khulis, University of Jeddah, Jeddah, Saudi Arabia

6. Department of Electrical Engineering, College of Engineering, Majmaah University, Majmaah, Saudi Arabia

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.

Funder

The Research & Innovation, Ministry of Education in Saudi Arabia

Publisher

PeerJ

Subject

General Computer Science

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