Scalable Zero-Knowledge Proofs for Verifying Cryptographic Hashing in Blockchain Applications

• URL: http://arxiv.org/abs/2407.03511v1
• Date: Wed, 3 Jul 2024 21:19:01 GMT
• Title: Scalable Zero-Knowledge Proofs for Verifying Cryptographic Hashing in Blockchain Applications
• Authors: Oleksandr Kuznetsov, Anton Yezhov, Vladyslav Yusiuk, Kateryna Kuznetsova,
• Abstract summary: 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.
• Score: 16.72979347045808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, specifically focusing on the SHA-256 algorithm. By leveraging the Plonky2 framework, which implements the PLONK protocol with FRI commitment scheme, we demonstrate the efficiency and scalability of our approach for both random data and real data blocks from the NEAR blockchain. The experimental results show consistent performance across different data sizes and types, with the time required for proof generation and verification remaining within acceptable limits. The generated circuits and proofs maintain manageable sizes, even for real-world data blocks with a large number of transactions. The proposed methodology contributes to the development of secure and trustworthy blockchain systems, where the integrity of computations can be verified without revealing the underlying data. Further research is needed to assess the applicability of the approach to other cryptographic primitives and to evaluate its performance in more complex real-world scenarios.

Related papers

• The Latency Price of Threshold Cryptosystem in Blockchains [52.359230560289745]
  We study the interplay between threshold cryptography and a class of blockchains that use Byzantine-fault tolerant (BFT) consensus protocols. Existing approaches for threshold cryptosystems introduce a latency overhead of at least one message delay for running the threshold cryptographic protocol. We propose a mechanism to eliminate this overhead for blockchain-native threshold cryptosystems with tight thresholds.
  arXiv  Detail & Related papers  (2024-07-16T20:53:04Z)
• 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)
• Enhanced Security and Efficiency in Blockchain with Aggregated Zero-Knowledge Proof Mechanisms [15.034624246970154]
  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.
  arXiv  Detail & Related papers  (2024-02-06T09:26:46Z)
• 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)
• SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers [50.608828039206365]
  We propose SOCI+ which significantly improves the performance of SOCI. SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive. Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.4 times more efficient in computation and 40% less in communication overhead.
  arXiv  Detail & Related papers  (2023-09-27T05:19:32Z)
• FedChain: An Efficient and Secure Consensus Protocol based on Proof of Useful Federated Learning for Blockchain [0.3480973072524161]
  The core of the blockchain is the consensus protocol, which establishes consensus among all the participants. We propose an efficient and secure consensus protocol based on proof of useful federated learning for blockchain (called FedChain) Our approach has been tested and validated through extensive experiments, demonstrating its performance.
  arXiv  Detail & Related papers  (2023-08-29T08:04:07Z)
• Deploying ZKP Frameworks with Real-World Data: Challenges and Proposed Solutions [0.5584060970507506]
  We present Fact Fortress, an end-to-end framework for designing and deploying zero-knowledge proofs of general statements. Our solution leverages proofs of data provenance and auditable data access policies to ensure the trustworthiness of how sensitive data is handled.
  arXiv  Detail & Related papers  (2023-07-12T18:53:42Z)
• Post-Quantum VRF and its Applications in Future-Proof Blockchain System [13.386254282693335]
  A verifiable random function (VRF) is a powerful pseudo-random function that provides a non-interactively public verifiable proof for the correctness of its output. We propose a generic compiler to obtain the post-quantum VRF from the simple VRF solution using symmetric-key primitives. We show potential applications of a quantum-secure VRF, such as quantum-secure decentralized random beacon and lottery-based proof of stake consensus blockchain protocol.
  arXiv  Detail & Related papers  (2021-09-05T07:10:41Z)
• Privacy-Preserved Blockchain-Federated-Learning for Medical Image Analysis Towards Multiple Parties [5.296010468961924]
  This article designs a privacy-preserving framework based on federated learning and blockchain. In the first step, we train the local model by using the capsule network for the segmentation and classification of the COVID-19 images. In the second step, we secure the local model through the homomorphic encryption scheme.
  arXiv  Detail & Related papers  (2021-04-22T07:32:04Z)
• 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.