Balancing Data Privacy and Compliance in Blockchain-Based Financial Systems

Abstract

This study explores the balance between data privacy and regulatory compliance in blockchain-based financial systems, focusing on privacy-enhancing technologies (PETs) such as Zero-Knowledge Proofs (ZKPs) and multiparty computations (MPCs). Through a comprehensive methodology combining literature review, comparative analysis, and empirical testing on the Ethereum test network, the research reveals significant trade-offs. Implementing ZKPs increased transaction times from 5 seconds to 12 seconds and gas fees from 0.02 ETH to 0.05 ETH, while computational load rose by 60%, highlighting the impact on scalability and efficiency. Chi-Square tests and regression analysis uncovered notable algorithmic biases, with low-value accounts experiencing 15% fewer transaction approvals and small mining pools receiving 20% fewer rewards than larger counterparts. Additionally, MPCs, while offering robust privacy, increased communication overhead by 35%, posing scalability challenges. The study recommends adopting a tiered privacy approach, implementing basic privacy measures for low-sensitivity transactions, and advanced technologies like ZKPs for high-sensitivity transactions while optimizing ZKPs to reduce their computational burden and enhance transaction speeds, and integrating artificial intelligence to detect and mitigate algorithmic biases in blockchain systems. Future research should also explore hybrid privacy solutions that combine the strengths of different PETs, such as ZKPs and MPCs, to achieve both robust privacy and high efficiency. Furthermore, investigating quantum-resistant cryptographic methods is crucial to safeguarding blockchain systems against emerging threats. These insights provide valuable guidance for financial institutions, blockchain developers, and policymakers, promoting the development of blockchain-based financial systems that optimize data privacy while maintaining system performance and regulatory compliance.