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Cloud music resources-oriented secure data storage and defense using edge computing

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  • Hong Cao

    (Hunan City University)

Abstract

Research motivation: The aim is to study the secure storage of cloud music resources, especially the Secure Data Storage and Defense (SDSD) in the Edge Computing Ecosystem (ECE). The main problems and solutions: the present work builds an anti-malware propagation mechanism and proposes an Edge Devices (EDs)-oriented ant-malware propagation SDSD algorithm for user cloud music resources. The proposed ED-oriented SDSD model and algorithm are based on the Mean Field Game (MFD), realizing the combination of SDSD strategies in ECE with game theory. Research results: In the actual ECE, the anti-malware SDSD model can reduce the data loss by adjusting the network and ED parameters. Specifically, within 0 ~ 20s, the number of infected devices decreases gradually at a rate of about 4% over time. Users can minimize their data loss while minimizing their Computing Resource (CORE) consumption. The data loss decreases monotonically with time. When t > 20s, the Average Data Loss (ADL) is stable at 0. Research conclusion: the proposed SDSD model for users’ cloud music resources is efficient and practical in securely storing data in the ECE.

Suggested Citation

  • Hong Cao, 2022. "Cloud music resources-oriented secure data storage and defense using edge computing," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1242-1250, December.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:3:d:10.1007_s13198-022-01718-x
    DOI: 10.1007/s13198-022-01718-x
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