DecTest: A Decentralised Testing Architecture for Improving Data Accuracy of Blockchain Oracle

• URL: http://arxiv.org/abs/2404.13535v2
• Date: Mon, 18 Nov 2024 14:58:21 GMT
• Title: DecTest: A Decentralised Testing Architecture for Improving Data Accuracy of Blockchain Oracle
• Authors: Xueying Zeng, Youquan Xian, Chunpei Li, Zhengdong Hu, Aoxiang Zhou, Peng Liu,
• Abstract summary: We introduce a new Decentralized Testing architecture (DecTest) 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%.
• Score: 5.327976961338759
• License:
• Abstract: Blockchain technology ensures secure and trustworthy data flow between multiple participants on the chain, but interoperability of on-chain and off-chain data has always been a difficult problem that needs to be solved. To solve the problem that blockchain systems cannot access off-chain data, oracle is introduced. However, existing research mainly focuses on the consistency and integrity of data, but ignores the problem that oracle nodes may be externally attacked or provide false data for selfish motives, resulting in the unresolved problem of data accuracy. In this paper, we introduce a new Decentralized Testing architecture (DecTest) that aims to improve data accuracy. A blockchain oracle random secret testing mechanism is first proposed to enhance the monitoring and verification of nodes by introducing a dynamic anonymized question-verification committee. Based on this, a comprehensive evaluation incentive mechanism is designed to incentivize honest work performance by evaluating nodes based on their reputation scores. The simulation results show that we successfully reduced the discrete entropy value of the acquired data and the real value of the data by 61.4%.

Related papers

• 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)
• Addressing Trust Challenges in Blockchain Oracles Using Asymmetric Byzantine Quorums [0.5461938536945723]
  A third-party interface or what is known as an Oracle is needed to interact with the external world. The genuineness of the data sourced by these Oracles is paramount, as it directly influences the Byzantine's reliability, credibility, and scalability. A strategy rooted in fault tolerance phi is introduced to tackle these challenges. An autonomous system for sustainability and audibility, built on detection, is put forth.
  arXiv  Detail & Related papers  (2023-12-30T08:53:45Z)
• RecAGT: Shard Testable Codes with Adaptive Group Testing for Malicious Nodes Identification in Sharding Permissioned Blockchain [8.178194928962311]
  We propose RecAGT, a novel identification scheme aimed at reducing communication overhead and identifying potential malicious nodes. First, shard testable codes are designed to encode the original data in case of a leak of confidential data. Second, a new identity proof protocol is presented as evidence against malicious behavior. Third, adaptive group testing is chosen to identify malicious nodes.
  arXiv  Detail & Related papers  (2023-11-05T07:43:48Z)
• A Trustworthy and Consistent Blockchain Oracle Scheme for Industrial Internet of Things [7.430160508879777]
  This paper proposes a secure and reliable oracle scheme that can obtain high-quality off-chain data. Specifically, we first design an oracle node selection algorithm based on Verifiable Random Function (VRF) and reputation mechanism. Second, we propose a data filtering algorithm based on a sliding window to further improve the consistency of the collected data.
  arXiv  Detail & Related papers  (2023-10-08T02:44:29Z)
• ACon$^2$: Adaptive Conformal Consensus for Provable Blockchain Oracles [31.439376852065713]
  Power of smart contracts is enabled by interacting with off-chain data, which in turn opens the possibility to undermine the block state consistency. We propose an adaptive conformal consensus (ACon$2$) algorithm, which derives consensus from multiple oracle contracts. In particular, the proposed algorithm returns a consensus set, which quantifies the uncertainty of data and achieves a desired correctness guarantee.
  arXiv  Detail & Related papers  (2022-11-17T04:37:24Z)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation [119.19061102064497]
  We propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL) In a round of the proposed BLADE-FL, each client broadcasts its trained model to other clients, competes to generate a block based on the received models, and then aggregates the models from the generated block before its local training of the next round. We explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.
  arXiv  Detail & Related papers  (2021-01-18T07:19:08Z)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients [124.48732110742623]
  We propose a novel framework by integrating blockchain into Federated Learning (FL) BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning. It gives rise to a new problem of training deficiency, caused by lazy clients who plagiarize others' trained models and add artificial noises to conceal their cheating behaviors.
  arXiv  Detail & Related papers  (2020-12-02T12:18:27Z)
• 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.