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An in-depth and systematic literature review on the blockchain-based approaches for cloud computing

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Abstract

Cloud computing enables businesses to decrease the total costs by outsourcing their required services. Therefore, it provides a new challenge of data protection regarding reliability, integrity, and confidentiality because of outsourcing. As a result, cloud security is becoming a key differentiator and competitive edge between cloud providers. Nowadays, the use of blockchain in cloud computing is one of the most common innovations that can solve cloud computing security problems. Blockchain is a decentralized data management technology to provide security, anonymity, and data integrity without any third-party organization. This work presents a comprehensive study of cloud computing security concerns and blockchain solutions to address these concerns. Applying different filters and searching international databases, 21 articles from reputable journals are found and reviewed. We examine cloud security in three categories: data integration, trust, and privacy. The results show that blockchain provides a successful platform in this regard. However, security issues are one of the most important challenges that still need further study. The article also provides a roadmap framework for future research and action.

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Notes

  1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

References

  1. Pavithran, D., et al.: Towards building a blockchain framework for IoT. Clust. Comput. 23(3), 2089–2103 (2020)

    Article  Google Scholar 

  2. Balasubramaniam, A., et al.: Blockchain for intelligent transport system. IETE Tech Rev 1–12 (2020).

  3. Tosh, D.K., et al.: Security implications of blockchain cloud with analysis of block withholding attack. In: 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE.

  4. Tarkhanov, I., Fomin-Nilov, D., Fomin, M.: Application of public blockchain to control the immutability of data in online scientific periodicals. Library Hi Tech (2019)

  5. Hasan, M.R., et al.: The applicability of blockchain technology in healthcare contexts to contain COVID-19 challenges. Library Hi Tech (2021)

  6. Dehghani, M., et al.: Blockchain-based securing of data exchange in a power transmission system considering congestion management and social welfare. Sustainability 13(1), 90 (2021)

    Article  MathSciNet  Google Scholar 

  7. Dehghani, M., et al.: Blockchain-based securing of data exchange in a power transmission system considering congestion management and social welfare. Sustainability 13(1), 1–1 (2020)

    Article  Google Scholar 

  8. Lo, S.K., et al.: Analysis of blockchain solutions for IoT: a systematic literature review. IEEE Access 7, 58822–58835 (2019)

    Article  Google Scholar 

  9. Ahram, T., et al.: Blockchain technology innovations. In: 2017 IEEE technology & engineering management conference (TEMSCON). IEEE.

  10. Khalid, U., et al.: A decentralized lightweight blockchain-based authentication mechanism for IoT systems. Cluster Comput. 1–21 (2020)

  11. Park, J.H., Park, J.H.: Blockchain security in cloud computing: use cases, challenges, and solutions. Symmetry 9(8), 164 (2017)

    Article  Google Scholar 

  12. Yu, Z., et al.: Systematic literature review on the security challenges of blockchain in IoT-based smart cities. Kybernetes (2021).

  13. Dillon, T., Wu, C., Chang, E.: Cloud computing: issues and challenges. In: 2010 24th IEEE International Conference on Advanced Information Networking and Applications. IEEE (2010)

  14. Rahimi, M., et al.: Toward the efficient service selection approaches in cloud computing. Kybernetes (2021).

  15. Wang, L., et al.: Cloud computing: a perspective study. N. Gener. Comput. 28(2), 137–146 (2010)

    Article  MATH  Google Scholar 

  16. Souri, A., et al.: A hybrid formal verification approach for QoS-aware multi-cloud service composition. Clust. Comput. 23(4), 2453–2470 (2020)

    Article  Google Scholar 

  17. Qian, L., et al.: Cloud computing: an overview. In: IEEE International Conference on Cloud Computing. Springer, Berlin (2009)

  18. Wang, Z.: Security and privacy issues within the cloud computing. In 2011 International Conference on Computational and Information Sciences. IEEE (2011).

  19. Scale, M.S.E.: Cloud computing and collaboration. Library Hi Tech News (2009)

  20. Yuan, Z., et al.: Probabilistic decomposition-based security constrained transmission expansion planning incorporating distributed series reactor. IET Gener. Transm. Distrib. 14(17), 3478–3487 (2020)

    Article  Google Scholar 

  21. Wang, H., Zhang, J.: Blockchain based data integrity verification for large-scale IoT data. IEEE Access 7, 164996–165006 (2019)

    Article  Google Scholar 

  22. Sharma, P., **dal, R., Borah, M.D.: Blockchain technology for cloud storage: a systematic literature review. ACM Comput. Surv. (CSUR) 53(4), 1–32 (2020)

    Article  Google Scholar 

  23. Alkadi, O., Moustafa, N., Turnbull, B.: A review of intrusion detection and blockchain applications in the cloud: approaches, challenges and solutions. IEEE Access 8, 104893–104917 (2020)

    Article  Google Scholar 

  24. Mughal, A., Joseph, A.: Blockchain for cloud storage security: a review. In: 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE (2020).

  25. Patel, P., Patel, H.: Review of blockchain technology to address various security issues in cloud computing. In: Data science and intelligent applications, pp. 345–354. Springer, Berlin (2021)

    Chapter  Google Scholar 

  26. **e, S., et al.: Blockchain for cloud exchange: a survey. Comput. Electr. Eng. 81, 106526 (2020)

    Article  Google Scholar 

  27. Gai, K., et al.: Blockchain meets cloud computing: a survey. IEEE Commun. Surv. Tutor. 22(3), 2009–2030 (2020)

    Article  Google Scholar 

  28. Pavithra, S., Ramya, S., Prathibha, S.: A survey on cloud security issues and blockchain. In: 2019 3rd International Conference on Computing and Communications Technologies (ICCCT). IEEE (2019)

  29. Memon, R.A., et al.: Cloud-based vs blockchain-based IoT: a comparative survey and way forward. Front. Inf. Technol. Electron. Eng. 21, 563–586 (2020).

  30. Murthy, C.V.B., Shri, M.L.: A survey on integrating cloud computing with blockchain. In: 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). IEEE (2020).

  31. Xu, H., et al.: A survey: Cloud data security based on blockchain technology. In: 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC). IEEE (2019).

  32. Prianga, S., Sagana, R., Sharon, E.: Evolutionary survey on data security in cloud computing using blockchain. In: 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA). IEEE (2018)

  33. Gill, S.S., et al.: Transformative effects of IoT, blockchain and artificial intelligence on cloud computing: evolution, vision, trends and open challenges. Internet Things 8, 100118 (2019)

    Article  Google Scholar 

  34. Vahdat, S.: Vitamin D and kidney diseases: a narrative review. Int. J. Prev. Med. 11(1), 195 (2020)

    Google Scholar 

  35. Mostafaie, T., et al.: A systematic study on meta-heuristic approaches for solving the graph coloring problem. 120, 104850 (2020)

    MathSciNet  Google Scholar 

  36. Hall, K., Harding, A.: A Systematic Review of Effective Literacy Teaching in the 4 to 14 Age Range of Mainstream Schooling, vol. 18, p. 2005. EPPI-Centre, Social Science Research Unit, Institute of Education. Retrieved September, London (2003).

  37. Heidari, A., Navimipour, J.N.: Service Discovery Mechanisms in the Cloud Computing: A Comprehensive and Systematic Literature Review. Kybernetes (2022).

  38. Mohammadian, V., et al.: Comprehensive and systematic study on the fault tolerance architectures in cloud computing. J. Circuits Syst. Comput. 29(15), 2050240 (2020)

    Article  Google Scholar 

  39. Vahdat, S., Shahidi, S.: D-dimer levels in chronic kidney illness: a comprehensive and systematic literature review. Proc. Natl. Acad. Sci. India Sect. B 1–18

  40. Guba, E.G., Lincoln, Y.S.: Fourth Generation Evaluation. Sage (1989).

  41. Singh, I., Lee, S.-W.: Comparative requirements analysis for the feasibility of blockchain for secure cloud. In: Asia Pacific Requirements Engeneering Conference. Springer (2017)

  42. Liang, X., et al.: Provchain: a blockchain-based data provenance architecture in cloud environment with enhanced privacy and availability. In: 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE (2017).

  43. Zhang, Y., et al.: Outsourcing service fair payment based on blockchain and its applications in cloud computing. IEEE Trans. Serv. Comput. (2018).

  44. Kirkman, S., Newman, R.: A cloud data movement policy architecture based on smart contracts and the ethereum blockchain. In: 2018 IEEE International Conference on Cloud Engineering (IC2E). IEEE (2018).

  45. Kumar, M., Singh, A.K., Kumar, T.S.: Secure log storage using blockchain and cloud infrastructure. In: 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE (2018).

  46. Wang, Y., et al.: Cloud-assisted EHR sharing with security and privacy preservation via consortium blockchain. IEEE Access 7, 136704–136719 (2019)

    Article  Google Scholar 

  47. Wang, J., et al.: Public auditing of log integrity for cloud storage systems via blockchain. In: International Conference on Security and Privacy in New Computing Environments. Springer, Berlin (2019).

  48. Chen, B., et al.: A blockchain-based searchable public-key encryption with forward and backward privacy for cloud-assisted vehicular social networks. IEEE Trans. Veh. Technol. 69(6), 5813–5825 (2019)

    Article  Google Scholar 

  49. Al Omar, A., et al.: Privacy-friendly platform for healthcare data in cloud based on blockchain environment. Futur. Gener. Comput. Syst. 95, 511–521 (2019)

    Article  Google Scholar 

  50. Zhu, L., et al.: Controllable and trustworthy blockchain-based cloud data management. Futur. Gener. Comput. Syst. 91, 527–535 (2019)

    Article  Google Scholar 

  51. Wilczyński, A., Kołodziej, J.: Modelling and simulation of security-aware task scheduling in cloud computing based on Blockchain technology. Simul. Model. Pract. Theory. 99, 102038 (2020)

    Article  Google Scholar 

  52. Ashik, M.H., Maswood, M.M.S., Alharbi, A.G.: Designing a Fog-Cloud architecture using blockchain and analyzing security improvements. In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). IEEE (2020)

  53. Medhane, D.V., et al.: Blockchain-enabled distributed security framework for next-generation IoT: an edge cloud and software-defined network-integrated approach. IEEE Internet Things J. 7(7), 6143–6149 (2020)

    Article  Google Scholar 

  54. Velmurugadass, P., et al. (2020) Enhancing Blockchain security in cloud computing with IoT environment using ECIES and cryptography hash algorithm. Mater. Today (2020).

  55. Rimba, P., et al.: Quantifying the cost of distrust: comparing blockchain and cloud services for business process execution. Inf. Syst. Front. 22(2), 489–507 (2020)

    Article  Google Scholar 

  56. Alkadi, O., et al.: A deep blockchain framework-enabled collaborative intrusion detection for protecting iot and cloud networks. IEEE Internet Things J. (2020).

  57. Wei, P., et al.: Blockchain data-based cloud data integrity protection mechanism. Futur. Gener. Comput. Syst. 102, 902–911 (2020)

    Article  Google Scholar 

  58. Huang, P., et al.: A collaborative auditing blockchain for trustworthy data integrity in cloud storage system. IEEE Access 8, 94780–94794 (2020)

    Article  Google Scholar 

  59. Yue, D., et al.: Blockchain-based verification framework for data integrity in edge-cloud storage. J. Parallel Distrib. Comput. 146, 1–14 (2020)

    Article  Google Scholar 

  60. He, K., et al.: Blockchain based data integrity verification for cloud storage with T-Merkle tree. In: International Conference on Algorithms and Architectures for Parallel Processing. Springer, Berlin (2020).

  61. Kurkin, A.V., Giraev, A.V., Medzhidov, Z.U.: Corporate database management on the basis of cloud technologies, blockchain technologies and technologies of big data processing: effectiveness and security. In: State and corporate management of region’s development in the conditions of the digital economy, pp. 79–83. Springer, Berlin (2021)

    Chapter  Google Scholar 

  62. Zhao, C., et al.: Secure consensus of multi-agent systems with redundant signal and communication interference via distributed dynamic event-triggered control. ISA Trans. 112, 89–98 (2021)

    Article  Google Scholar 

  63. Heidari, A., Navimipour, J.N.: A new SLA-aware method for discovering the cloud services using an improved nature-inspired optimization algorithm. PeerJ Comput. Sci. (2021).

  64. Lu, M., et al.: Smart load scheduling strategy utilising optimal charging of electric vehicles in power grids based on an optimisation algorithm (2020)

  65. Khan, K.M., Malluhi, Q.: Establishing trust in cloud computing. IT Prof. 12(5), 20–27 (2010)

    Article  Google Scholar 

  66. Li, R., et al.: Trust Mechanism of cloud manufacturing service platform based on blockchain. In: 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE (2019).

  67. Zhang, Y., et al.: Towards augmented kernel extreme learning models for bankruptcy prediction: algorithmic behavior and comprehensive analysis. Neurocomputing 430, 185–212 (2021)

    Article  Google Scholar 

  68. Shen, L., et al.: Evolving support vector machines using fruit fly optimization for medical data classification. Knowl.-Based Syst. 96, 61–75 (2016)

    Article  Google Scholar 

  69. Yue, D., et al.: Blockchain based data integrity verification in P2P cloud storage. In: 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS). IEEE (2018).

  70. Heidari, A., et al.: Internet of Things offloading: ongoing issues, opportunities, and future challenges. Int. J. Commun. Syst. 33(14), e4474.

  71. Sheng, H., et al.: Near-online tracking with co-occurrence constraints in blockchain-based edge computing. IEEE Internet Things J. 8(4), 2193–2207 (2020)

    Article  Google Scholar 

  72. Li, L., et al.: Predictive cloud control for multiagent systems with stochastic event-triggered schedule. ISA Trans. 94, 70–79 (2019)

    Article  Google Scholar 

  73. Soltanisehat, L., et al.: Technical, temporal, and spatial research challenges and opportunities in blockchain-based healthcare: a systematic literature review. IEEE Trans. Eng. Manag. (2020)

  74. Coutinho, E.F., et al.: Towards cloud computing and blockchain integrated applications. In: 2020 IEEE International Conference on Software Architecture Companion (ICSA-C). IEEE (2020).

  75. Zhao, Y., Duncan, B.: The impact of crypto-currency risks on the use of blockchain for cloud security and privacy. In: 2018 International Conference on High Performance Computing & Simulation (HPCS). IEEE (2018).

  76. Lv, Z., et al.: Analysis of using blockchain to protect the privacy of drone big data. IEEE Netw. 35(1), 44–49 (2021)

    Article  Google Scholar 

  77. Zhao, G., et al.: Blockchain technology in agri-food value chain management: a synthesis of applications, challenges and future research directions. Comput. Ind. 109, 83–99 (2019)

    Article  Google Scholar 

  78. Gai, K., Choo, K.-K.R., Zhu, L.: Blockchain-enabled reengineering of cloud datacenters. IEEE Cloud Comput. 5(6), 21–25 (2018)

    Article  Google Scholar 

  79. Gill, S.S.: Quantum and blockchain based Serverless edge computing: a vision, model, new trends and future directions. Internet Technol. Lett. e275 (2021).

  80. Lv, Z., Chen, D., Wang, Q.: Diversified technologies in internet of vehicles under intelligent edge computing. IEEE Trans. Intell. Transp. Syst. 22(4), 2048–2059 (2020)

    Article  Google Scholar 

  81. Hu, J., et al.: Orthogonal learning covariance matrix for defects of grey wolf optimizer: insights, balance, diversity, and feature selection. Knowl. Based Syst. 213, 106684 (2021)

    Article  Google Scholar 

  82. Zhang, Y., et al.: Boosted binary Harris hawks optimizer and feature selection. Eng. Comput. 1–30 (2020).

  83. Zhao, X., et al.: Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton. Appl. Soft Comput. 24, 585–596 (2014)

    Article  Google Scholar 

  84. Weng, L., et al.: Deep cascading network architecture for robust automatic modulation classification. Neurocomputing 455, 308–324 (2021)

    Article  Google Scholar 

  85. He, Y., Dai, L., Zhang, H.: Multi-branch deep residual learning for clustering and beamforming in user-centric network. IEEE Commun. Lett. 24(10), 2221–2225 (2020)

    Article  Google Scholar 

  86. Lv, Z., et al.: Fine-grained visual computing based on deep learning. ACM Trans. Multimidia Comput. Commun. Appl. 17(1s), 1–19 (2021)

    Article  Google Scholar 

  87. Esposito, C., et al.: Blockchain: a panacea for healthcare cloud-based data security and privacy? IEEE Cloud Comput. 5(1), 31–37 (2018)

    Article  Google Scholar 

  88. Shan, W., et al.: Double adaptive weights for stabilization of moth flame optimizer: balance analysis, engineering cases, and medical diagnosis. Knowl. Based Syst. 214, 106728 (2021).

  89. Wang, M., Chen, H.: Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl. Soft Comput. 88, 105946 (2020)

    Article  Google Scholar 

  90. Wang, M., et al.: Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 267, 69–84 (2017)

    Article  Google Scholar 

  91. Ahadi, A., Ghadimi, N., Mirabbasi, D.: An analytical methodology for assessment of smart monitoring impact on future electric power distribution system reliability. Complexity 21(1), 99–113 (2015)

    Article  Google Scholar 

  92. Chen, H.-L., et al.: An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson’s disease. Neurocomputing 184, 131–144 (2016)

    Article  Google Scholar 

  93. **a, J., et al.: Ultrasound-based differentiation of malignant and benign thyroid Nodules: an extreme learning machine approach. Comput. Methods Programs Biomed. 147, 37–49 (2017)

    Article  Google Scholar 

  94. Zhao, C., et al.: Synchronization of Markovian complex networks with input mode delay and Markovian directed communication via distributed dynamic event-triggered control. Nonlinear Analysis: Hybrid Systems 36, 100883 (2020).

  95. **e, W., et al.: Strictly dissipative stabilization of multiple-memory Markov jump systems with general transition rates: a novel event-triggered control strategy. Int. J. Robust Nonlinear Control 30(5), 1956–1978 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  96. Zhao, C., et al.: Novel results on nonfragile sampled-data exponential synchronization for delayed complex dynamical networks. Int. J. Robust Nonlinear Control 30(10), 4022–4042 (2020)

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Jianhu Gong or Nima Jafari Navimipour.

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Gong, J., Navimipour, N.J. An in-depth and systematic literature review on the blockchain-based approaches for cloud computing. Cluster Comput 25, 383–400 (2022). https://doi.org/10.1007/s10586-021-03412-2

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