Abstract

Ensuring data integrity is critical in distributed systems, where multiple parties may access, modify, or share information. Traditional centralized approaches are often vulnerable to tampering, single points of failure, and trust deficiencies. Distributed ledger technologies (DLTs), including blockchain and directed acyclic graph (DAG)-based frameworks, offer a decentralized and tamper-resistant mechanism for verifying data integrity across multiple nodes. By providing cryptographic guarantees, immutability, and consensus-driven validation, DLTs ensure that data remains accurate, authentic, and consistent over time. This research explores distributed ledger frameworks for secure data integrity verification in multi-user and multi-organizational environments. It evaluates different ledger architectures, consensus protocols, and cryptographic mechanisms in terms of performance, scalability, and security. Additionally, it proposes an integrated framework that optimizes ledger deployment for efficient verification while minimizing latency and computational overhead. Simulation results and case studies demonstrate that DLT-based approaches significantly reduce the risk of data tampering, enable traceability, and enhance trust among participants. The study contributes to the design of resilient, scalable, and secure distributed systems suitable for applications in finance, healthcare, supply chain management, and governmental record-keeping