Graph-based access control models for cloud security
Archana Ugalearchanaugale11@gmail.comDepartment of Information TechnologySir Visvesvarya Institute of TechnologySavitribai Phule Pune University (SPPU)Nashik, Maharashtra, 422103, IndiaView full profile → , *Mittapalli PremchandhCorresponding authormittapallipremchandh@yahoo.comCentre for Multidisciplinary ResearchAnurag UniversityHyderabad, Telangana, 500088, India0009-0003-8862-3914View full profile → , Chetansinh R. Vaghelacrvaghela.maths.gsc@gokuluniversity.ac.inFaculty of ScienceGokul Global UniversitySidhpur, Gujarat, 384151, IndiaView full profile → , Sidhant Sanjay Kulkarnisidhantkulkarni@gmail.comDepartment of Mechatronics EngineeringSanjivani College of EngineeringSavitribai Phule Pune UniversityKopargaon, Ahilyanagar, Maharashtra, 423603, IndiaView full profile → , Ajay Kushwahaajay.kushwaha@rungta.orgDepartment of Computer Science & EngineeringRungta College of Engineering & TechnologyRungta International Skills UniversityBhilai, Chhattisgarh, 490024, IndiaView full profile → , Makineedi Raja Baburajababu.makineedi@adityauniversity.inDepartment of Information TechnologyAditya UniversitySurampalem, Kakinada District, Andhra Pradesh, 533437, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-3063
- Pages:
- 4429–4441
Abstract
The paper presents a graph-based access control model for cloud security which uses a formal graph structure to represent entities and their relationship. The proposed model describes cloud entities, including users, services, and data resources, as nodes in a graph (G) and access relationship among nodes are described using access relationships, including trust and cost attributes as edges. The measures of the performance of the proposed model include access decision time, computational complexity and the level of security. Experimental evidence and finding shows that the framework has lower access latency (around 12 ms) than RBAC (around 15 ms) and ABAC (around 22 ms), as well as a higher level of security effectiveness (around 92%) due to trust-aware validation and path-based control implementation. The model also has high scalability and dynamism in cloud environments. The graph-based framework proposed is an effective and efficient solution to next-generation cloud access control, and would be more effective than traditional methods both in terms of security and performance.
Keywords
Subject Classifications
References
[1] N. Almolhis, A. M. Alashjaee, S. Duraibi, F. Alqahtani, and A. N. Moussa, “The security issues in IoT-cloud: A review,” in Proc. 16th IEEE Int. Colloq. Signal Process. Its Appl. (CSPA), Langkawi, Malaysia, pp. 191–196 (Feb. 2020).
[2] S. Baccouri and T. Abdellatif, “BIG-ABAC: Leveraging big data for adaptive, scalable, and context-aware access control,” Comput. Model. Eng. Sci., vol. 143, pp. 1071–1093 (2025).
[3] F. Chen, D. Luo, T. Xiang, P. Chen, J. Fan, and H. L. Truong, “IoT cloud security review: A case study approach using emerging consumer-oriented applications,” ACM Comput. Surv., vol. 54 (2021).
[4] E. B. Fernandez and A. Brazhuk, “A critical analysis of Zero Trust Architecture (ZTA),” Comput. Stand. Interfaces, vol. 89, Art. no. 103832 (2024).
[5] L. Jyotsna and L. Praveen Kumar, “Enhanced image security through block permutation and modular arithmetic shuffling,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 29, no. 3, pp. 1313–1333 (2026), doi: 10.47974/JDMSC-2617.
[6] L. Golightly, P. Modesti, R. Garcia, and V. Chang, “Securing distributed systems: A survey on access control techniques for cloud, blockchain, IoT and SDN,” Cyber Secur. Appl., vol. 1, Art. no. 100015 (Dec. 2023).
[7] S. H. Jayasundara, N. A. G. Arachchilage, and G. Russello, “SoK: Access control policy generation from high-level natural language requirements,” ACM Comput. Surv., vol. 57, no. 4, pp. 1–37 (2024).
[8] G. Karatas and A. Akbulut, “Survey on access control mechanisms in cloud computing,” J. Cyber Secur. Mobility, vol. 7, no. 3, pp. 1–36 (2018), doi: 10.13052/2245-1439.731.
[9] A. Gaber, W. Wannes, and H. Zouari, “On the friable mean-values of some arithmetic functions,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 6, pp. 2337–2350 (2025), doi: 10.47974/JDMSC-2283.
[10] A. Kesarwani and P. M. Khilar, “Development of trust-based access control models using fuzzy logic in cloud computing,” J. King Saud Univ. Comput. Inf. Sci., vol. 34, pp. 1958–1967 (2022).
[11] Y. Lit, S. Kim, and E. Sy, “A survey on Amazon Alexa attack surfaces,” in Proc. IEEE 18th Annu. Consum. Commun. Netw. Conf. (CCNC), Las Vegas, NV, USA, pp. 1–7 (Jan. 2021).
[12] D. P. Chavan, “A Network-Centric Web Application for Dynamic Crop and Fertilizer Decision Support,” International Journal on Advanced Electrical and Computer Engineering, vol. 14, no. 2, pp. 9–15 (2025), doi: 10.65521/ijaece.v14i2.1279.
[13] S. Parkinson and S. Khan, “A survey on empirical security analysis of access-control systems: A real-world perspective,” ACM Comput. Surveys, vol. 55, no. 6, pp. 1–28 (Jul. 2023).
[14] A. M. Tall and C. C. Zou, “A framework for attribute-based access control in processing big data with multiple sensitivities,” Appl. Sci., vol. 13, Art. no. 1183 (2023).
[15] R. C. Poonia, T. Singh, C. K. Rajashri, D. Mondal, and S. M. Patil, “Optimizing communication system security through comprehensive testing and intelligent network configurations,” Journal of Information and Optimization Sciences, vol. 47, no. 5-A, pp. 1709–1717 (2026), doi: 10.47974/JIOS-2261.




