Securing Confidentiality in Distributed Ledger Systems with Secure Multi-party Computation for Financial Data Protection

This study addresses confidentiality challenges in financial Distributed Ledger Systems (DLS) using Secure Multi-Party Computation (SMPC). By analyzing real-world datasets, it evaluates privacy risks, protocol efficiency, and system resilience. Findings highlight SMPC’s role in enhancing security while balancing computational efficiency. Using the Elliptic AML Bitcoin Transactions dataset, anomaly detection (Isolation Forest) identifies financial confidentiality vulnerabilities, revealing that anomalous transactions exhibit a 336.1% increase in volume and a 15.5% rise in frequency, suggesting heightened risks. A comparative analysis of SMPC protocols utilizing the MP-SPDZ benchmark dataset and one-way ANOVA confirms that Yao’s Garbled Circuits is the most computationally efficient (180.50 ms execution time), whereas Shamir’s Secret Sharing offers superior security (0.73 high-probability security). Kaplan-Meier survival analysis of Verizon DBIR 2024 establishes that SMPC extends financial system longevity (36.11 months vs. 21.91 months for traditional encryption). Recommendations include integrating scalable SMPC models, standardizing regulatory frameworks, optimizing algorithmic efficiency, and enhancing anomaly detection in financial DLS.