Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 18 4 2026 10 01 A Trust-Aware and Fault-Tolerant Framework for Secure Data Sharing in Cloud–Edge Environments 1 17 108912 10.22059/jitm.2026.108912 EN Kalpana Dwivedi Ph.D. Candidate, Department of Computer Science and Engineering, SSCSE, Sharda University, Greater Noida, Uttar Pradesh 201310. Gaurav Raj Associate Prof., Department of Computer Science and Engineering, SSCSE, Sharda University, Greater Noida, Uttar Pradesh 201310. Journal Article The accelerated growth of cloud edge computing has increased the need for secure, reliable, and efficient data exchange in distributed and heterogeneous environments. However, node failures, untrustworthy edge devices, information corruption, key destruction, and excessive communication overhead are significant issues that significantly affect the performance and security of the system. To address these challenges, this paper proposes a trust-aware, fault-tolerant, and secure data-sharing framework for cloud–edge environments. The framework combines the use of dynamic trust evaluation, lightweight cryptography, and erasure codes for data distribution to ensure data confidentiality, integrity, availability, and trustworthiness. By a behavior-based trust model, the edge nodes are continually evaluated on parameters such as reliability, history of previous interactions, and consistency of services, allowing for intelligent decisions to be made regarding data placement and recovery. Erasure coding allows for reconstructing data against multiple node failures at a low storage overhead cost, which enables greater fault tolerance. Within the same framework, secure key management and integrity checks are provided against eavesdropping, replay attacks, insider attacks, and malicious node actions. Simulation results demonstrate that the proposed framework significantly outperforms conventional cloud–edge data-sharing solutions in terms of data availability, fault tolerance, latency, and bandwidth efficiency. The results demonstrate the suitability of the proposed framework for large-scale and security-sensitive applications, such as IoT data aggregation, healthcare, and enterprise cloud computing. https://jitm.ut.ac.ir/article_108912_f062af48cd10bc3a870303bc7c55a946.pdf