Enhanced Security and Efficiency in Blockchain with Aggregated Zero-Knowledge Proof Mechanisms

• URL: http://arxiv.org/abs/2402.03834v1
• Date: Tue, 6 Feb 2024 09:26:46 GMT
• Title: Enhanced Security and Efficiency in Blockchain with Aggregated Zero-Knowledge Proof Mechanisms
• Authors: Oleksandr Kuznetsov, Alex Rusnak, Anton Yezhov, Dzianis Kanonik, Kateryna Kuznetsova, Stanislav Karashchuk,
• Abstract summary: Current approaches to data verification in blockchain systems face challenges in terms of efficiency and computational overhead. This study proposes 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.
• Score: 15.034624246970154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Scalable Zero-Knowledge Proofs for Verifying Cryptographic Hashing in Blockchain Applications [16.72979347045808]
  Zero-knowledge proofs (ZKPs) have emerged as a promising solution to address the scalability challenges in modern blockchain systems. This study proposes a methodology for generating and verifying ZKPs to ensure the computational integrity of cryptographic hashing.
  arXiv  Detail & Related papers  (2024-07-03T21:19:01Z)
• Enhancing Data Integrity and Traceability in Industry Cyber Physical Systems (ICPS) through Blockchain Technology: A Comprehensive Approach [0.0]
  This study explores the potential of blockchain in enhancing data integrity and traceability within Industry Cyber-Physical Systems (ICPS) ICPS is pivotal in managing critical infrastructure like manufacturing, power grids, and transportation networks. This research unearths various blockchain applications in ICPS, including supply chain management, quality control, contract management, and data sharing.
  arXiv  Detail & Related papers  (2024-05-08T06:22:37Z)
• DesTest: A Decentralised Testing Architecture for Improving Data Accuracy of Blockchain Oracle [5.64560868386402]
  We introduce a new decentralized testing architecture (DesTest) that aims to improve data accuracy. A blockchain oracle random secret testing mechanism is first proposed to enhance the monitoring and verification of nodes. We successfully reduced the discrete entropy value of the acquired data and the real value of the data by 61.4%.
  arXiv  Detail & Related papers  (2024-04-21T05:10:17Z)
• Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
  Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy. This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
  arXiv  Detail & Related papers  (2024-03-28T07:08:26Z)
• Graph Attention Network-based Block Propagation with Optimal AoI and Reputation in Web 3.0 [59.94605620983965]
  We design a Graph Attention Network (GAT)-based reliable block propagation optimization framework for blockchain-enabled Web 3.0. To achieve the reliability of block propagation, we introduce a reputation mechanism based on the subjective logic model. Considering that the GAT possesses the excellent ability to process graph-structured data, we utilize the GAT with reinforcement learning to obtain the optimal block propagation trajectory.
  arXiv  Detail & Related papers  (2024-03-20T01:58:38Z)
• Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study [73.87110604150315]
  Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
  arXiv  Detail & Related papers  (2024-01-28T10:46:17Z)
• Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach [69.62333053044712]
  We propose the use of explainable artificial intelligence for decentralised computing of estimated contributions to a metric of interest. This approach mitigates the need to convince supply chain actors to share data, as all computations occur in a decentralised manner. Results demonstrate the effectiveness of our approach in detecting the source of quality variations compared to a centralised approach.
  arXiv  Detail & Related papers  (2023-07-22T20:03:16Z)
• Blockchain Large Language Models [65.7726590159576]
  This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions. The proposed tool, BlockGPT, generates tracing representations of blockchain activity and trains from scratch a large language model to act as a real-time Intrusion Detection System.
  arXiv  Detail & Related papers  (2023-04-25T11:56:18Z)
• Identity Inference on Blockchain using Graph Neural Network [5.5927440285709835]
  Identity inference, which aims to make a preliminary inference about account identity, plays a significant role in blockchain security. We present a novel approach to analyze user's behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern. We also propose a generic end-to-end graph neural network model, named $textI2 textBGNN$, which can accept subgraph as input and learn a function mapping the transaction subgraph pattern to account identity.
  arXiv  Detail & Related papers  (2021-04-14T00:15:38Z)
• Faster Secure Data Mining via Distributed Homomorphic Encryption [108.77460689459247]
  Homomorphic Encryption (HE) is receiving more and more attention recently for its capability to do computations over the encrypted field. We propose a novel general distributed HE-based data mining framework towards one step of solving the scaling problem. We verify the efficiency and effectiveness of our new framework by testing over various data mining algorithms and benchmark data-sets.
  arXiv  Detail & Related papers  (2020-06-17T18:14:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.