Multi‑Cloud Secured Data Fragmentation and Retrieval (MC‑SDFR): A Framework for Confidentiality, Access Control, and Traceability

Abstract

The rapid growth of sensitive data across distributed multi‑cloud environments calls for storage architectures that are scalable, compliance‑aware, and tamper‑resistant. Existing fragmentation‑based protections often incur high overhead, apply uniform encryption regardless of sensitivity, and provide limited auditability across multiple jurisdictions. This paper presents Multi-Cloud Secured Data Fragmentation and Retrieval Framework (MC‑SDFR)—a modular framework that combines (i) an Adaptive Sensitivity‑aware Fragmentation Technique (ASFT) for dynamic fragment shaping and selective encryption, (ii) a Multi‑Cloud Authorization and Verification Protocol (MC‑AVP) for attribute‑driven access control with federated identity, and (iii) a Decentralized Integrity and Traceability Mechanism (DITM) using a permissioned blockchain for immutable logging and deterministic verification. Across 64–1024 MB workloads, MC‑SDFR reduces fragmentation time by up to 25%–26%, defragmentation time by up to 36%, and combined encryption/decryption latency by ≈30%–40% compared with a baseline linear approach. Overhead ratios remain consistently below 0.9 at 1 GB. DITM achieves ≥99.5% tamper‑detection accuracy with negligible false‑positive rates. These results indicate that MC‑SDFR is a practical, performance‑optimized approach for secure, compliant data management in heterogeneous multi‑cloud and edge settings.