Enhanced Security and Efficiency in Blockchain with Aggregated Zero-Knowledge Proof Mechanisms
<|reference_start|>Enhanced Security and Efficiency in Blockchain with Aggregated Zero-Knowledge Proof Mechanisms: Blockchain technology has emerged as a revolutionary tool in ensuring data integrity and security in digital transactions. However, the current approaches to data verification in blockchain systems, particularly in Ethereum, face challenges in terms of efficiency and computational overhead. The traditional use of Merkle Trees and cryptographic hash functions, while effective, leads to significant resource consumption, especially for large datasets. This highlights a gap in existing research: the need for more efficient methods of data verification in blockchain networks. Our study addresses this gap by proposing an innovative aggregation scheme for Zero-Knowledge Proofs within the structure of Merkle Trees. We develop a system that significantly reduces the size of the proof and the computational resources needed for its generation and verification. Our approach represents a paradigm shift in blockchain data verification, balancing security with efficiency. We conducted extensive experimental evaluations using real Ethereum block data to validate the effectiveness of our proposed scheme. The results demonstrate a drastic reduction in proof size and computational requirements compared to traditional methods, making the verification process more efficient and economically viable. Our contribution fills a critical research void, offering a scalable and secure solution for blockchain data verification. The implications of our work are far-reaching, enhancing the overall performance and adaptability of blockchain technology in various applications, from financial transactions to supply chain management.<|reference_end|>
@article{kuznetsov2024enhanced,
    title={Enhanced Security and Efficiency in Blockchain with Aggregated
  Zero-Knowledge Proof Mechanisms},
    author={Oleksandr Kuznetsov, Alex Rusnak, Anton Yezhov, Dzianis Kanonik,
  Kateryna Kuznetsova, Stanislav Karashchuk},
    journal={IEEE Access, vol. 12, 2024},
    year={2024},
    doi={10.1109/ACCESS.2024.3384705},
    archivePrefix={arXiv},
    eprint={2402.03834},
    primaryClass={cs.CR}
}