Securing Educational Financial Data Against Insider Threats: A Hybrid Blockchain Approach with Merkle Tree Aggregation

Abstract

Financial information systems in educational institutions face insider threats where privileged administrators can manipulate database records undetected by conventional security. This study proposes a Hybrid Blockchain architecture integrating Merkle Tree Aggregation and a Reversal Entry mechanism to establish a tamper-evident financial audit trail and address these data integrity gaps. Developed via the Design Science Research Methodology (DSRM), the system implements three cryptographic layers: a SHA-256 Recursive Transaction Hash Chain for local integrity, a Keccak-256 Merkle Tree for daily aggregation, and public Ethereum anchoring. Empirical evaluations demonstrate successful cryptographic integrity verification across all five database manipulation attack scenarios with zero false positives only under the five tested normal operational scenarios, relying strictly on deterministic hashing rather than AI-based anomaly detection. Computational latency remains consistent at 3.45 ms per transaction. Based on Sepolia testnet data under mainnet-equivalent projections, the 1,000:1 aggregation compression yields 99.90% cost efficiency compared to pure public blockchains, with sensitivity analysis confirming financial viability across volatile gas prices and exchange rates. These findings indicate the technical and economic feasibility of adopting a Hybrid Blockchain for detecting tampering and preserving data integrity in educational institutional financial data under the evaluated scenario.