Towards Robust Blockchain Price Oracle: A Study on Human-Centric Node Selection Strategy and Incentive Mechanism

• URL: http://arxiv.org/abs/2309.04689v2
• Date: Wed, 16 Oct 2024 13:44:26 GMT
• Title: Towards Robust Blockchain Price Oracle: A Study on Human-Centric Node Selection Strategy and Incentive Mechanism
• Authors: Youquan Xian, Xueying Zeng, Hao Wu, Danping Yang, Peng Wang, Peng Liu,
• Abstract summary: oracle can obtain trusted real-time price information for financial applications such as payment and settlement, and asset valuation on the blockchain. This paper proposes an anonymous node selection scheme that anonymously selects nodes with high reputations to participate in tasks. Under the hypothesis of rational man, an incentive mechanism based on the Stackelberg game is proposed.
• Score: 6.524599041387636
• License:
• Abstract: As a trusted middleware connecting the blockchain and the real world, the blockchain oracle can obtain trusted real-time price information for financial applications such as payment and settlement, and asset valuation on the blockchain. However, the current oracle schemes face the dilemma of security and service quality in the process of node selection, and the implicit interest relationship in financial applications leads to a significant conflict of interest between the task publisher and the executor, which reduces the participation enthusiasm of both parties and system security. Therefore, this paper proposes an anonymous node selection scheme that anonymously selects nodes with high reputations to participate in tasks to ensure the security and service quality of nodes. Then, this paper also details the interest requirements and behavioral motives of all parties in the payment settlement and asset valuation scenarios. Under the hypothesis of rational man, an incentive mechanism based on the Stackelberg game is proposed. It can achieve equilibrium under the pursuit of the revenue of task publishers and executors, thereby ensuring the revenue of all types of users and improving the enthusiasm for participation. Finally, we verify the security of the proposed scheme through security analysis. The experimental results show that the proposed scheme can reduce the variance of obtaining price data by about 55\% while ensuring security, and meeting the revenue of all parties.

Related papers

• Deceptive Sequential Decision-Making via Regularized Policy Optimization [54.38738815697299]
  Two regularization strategies for policy synthesis problems that actively deceive an adversary about a system's underlying rewards are presented. We show how each form of deception can be implemented in policy optimization problems. We show that diversionary deception can cause the adversary to believe that the most important agent is the least important, while attaining a total accumulated reward that is $98.83%$ of its optimal, non-deceptive value.
  arXiv  Detail & Related papers  (2025-01-30T23:41:40Z)
• Balancing Confidentiality and Transparency for Blockchain-based Process-Aware Information Systems [46.404531555921906]
  We propose an architecture for blockchain-based PAISs aimed at preserving both confidentiality and transparency. Smart contracts enact, enforce and store public interactions, while attribute-based encryption techniques are adopted to specify access grants to confidential information.
  arXiv  Detail & Related papers  (2024-12-07T20:18:36Z)
• A Game-Theoretic Approach to the Study of Blockchain's Robustness [0.0]
  This thesis investigates the robustness of blockchain protocols, specifically focusing on Proof-of-Stake. We define robustness in terms of two critical properties: Safety, which ensures that the blockchain will not have permanent conflicting blocks, and Liveness, which guarantees the continuous addition of new reliable blocks. Our research addresses the gap between traditional distributed systems approaches, which classify agents as either honest or Byzantine (i.e. malicious or faulty), and game-theoretic models that consider rational agents driven by incentives.
  arXiv  Detail & Related papers  (2024-11-28T14:29:14Z)
• Maximal Extractable Value in Decentralized Finance: Taxonomy, Detection, and Mitigation [0.534772724436823]
  Maximal Extractable Value (MEV) can be extracted from financial transactions on the blockchain. MEV causes financial losses and consensus instability, disrupting the security, efficiency, and decentralization goals of the DeFi ecosystem. This survey provides valuable insights for researchers, developers, stakeholders, and policymakers.
  arXiv  Detail & Related papers  (2024-10-22T12:11:41Z)
• Efficient and Universally Accessible Cross-Chain Options without Upfront Holder Collateral [3.5562096606353215]
  We present a protocol for efficient and universally accessible option trading. Our protocol's universality allows for cross-chain options involving nearly $textitany$ assets on $textitany$ two different blockchains. By introducing a guarantee from the option writer, our protocol removes the need of upfront collateral from holders.
  arXiv  Detail & Related papers  (2024-10-21T07:43:20Z)
• MEV Ecosystem Evolution From Ethereum 1.0 [6.151915040556504]
  In traditional finance, there are possibilities to create values, e.g., arbitrage offers to create value from market inefficiencies or front-running offers to extract value for the participants having privileged roles. Such opportunities are readily available in DeFi ecosystems, where diverse participants engage in financial activities. In this survey, first, we show how lucrative such opportunities can be. Then, we discuss how protocolfollowing participants trying to capture such opportunities threaten to sabotage blockchain's performance. Finally, we review the current state of research trying to restore trustlessness and decentralization to provide all DeFi participants with a fair marketplace
  arXiv  Detail & Related papers  (2024-06-19T14:22:26Z)
• 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)
• 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)
• 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)
• Incentive-Aware Recommender Systems in Two-Sided Markets [49.692453629365204]
  We propose a novel recommender system that aligns with agents' incentives while achieving myopically optimal performance. Our framework models this incentive-aware system as a multi-agent bandit problem in two-sided markets. Both algorithms satisfy an ex-post fairness criterion, which protects agents from over-exploitation.
  arXiv  Detail & Related papers  (2022-11-23T22:20:12Z)
• Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning [100.73223416589596]
  We propose a cost-sensitive portfolio selection method with deep reinforcement learning. Specifically, a novel two-stream portfolio policy network is devised to extract both price series patterns and asset correlations. A new cost-sensitive reward function is developed to maximize the accumulated return and constrain both costs via reinforcement learning.
  arXiv  Detail & Related papers  (2020-03-06T06:28:17Z)

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.