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A Review on Data Access Control Schemes in Mobile Cloud Computing: State-of-the-Art Solutions and Research Directions

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Abstract

Mobile cloud computing (MCC) offers users the flexibility and freedom to access the services or data outsourced in the cloud. MCC has witnessed rapid growth in both research and emerging applications. Lightweight and secure data access control is one of the most challenging security issues in MCC. Owing to the cryptographic-based access control property, ciphertext policy attribute-based encryption (CP-ABE) has been widely adopted as an effective solution for data access control in cloud computing. Nevertheless, CP-ABE cryptographic construct is based on bilinear paring rendering high computation cost. Therefore, using CP-ABE in MCC environment is non-trivial because mobile devices are resource-constraint and they are not suitable to run such high complexity of cryptographic operation. This research issue is unmatured and requires in-depth investigation. In this paper, we investigate the research problems and challenges of data access control solutions in MCC. Specifically, we conduct the comprehensive review of the-state-of-the-art CP-ABE based access control solutions used in MCC. The review is done through the analysis and discussion of key solutions. Finally, we point out possible research issues and challenges.

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Funding

This study was funded by the SIIT Young Researcher Grant under the contract no. SIIT2019-YRG-SF02.

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Authors and Affiliations

  1. School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12120, Thailand

    Somchart Fugkeaw & Pattavee Sanchol

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  1. Somchart Fugkeaw
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  2. Pattavee Sanchol
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Correspondence to Somchart Fugkeaw.

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This article is part of the topical collection “Advanced Machine Learning Approaches in Cognitive Computing” guest edited by Kuntpong Woraratpanya and Phayung Meesad.

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Fugkeaw, S., Sanchol, P. A Review on Data Access Control Schemes in Mobile Cloud Computing: State-of-the-Art Solutions and Research Directions. SN COMPUT. SCI. 3, 42 (2022). https://doi.org/10.1007/s42979-021-00917-w

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