Proof-of-Learning with Incentive Security

• URL: http://arxiv.org/abs/2404.09005v7
• Date: Wed, 08 Jan 2025 02:10:31 GMT
• Title: Proof-of-Learning with Incentive Security
• Authors: Zishuo Zhao, Zhixuan Fang, Xuechao Wang, Xi Chen, Hongxu Su, Haibo Xiao, Yuan Zhou,
• Abstract summary: Most concurrent blockchain systems rely heavily on the Proof-of-Work (PoW) or Proof-of-Stake (PoS) mechanisms for decentralized consensus and security assurance. PoUW seeks to employ challenges of practical significance as PoW, thereby imbuing energy consumption with tangible value. We introduce the concept of incentive-security that incentivizes rational provers to behave honestly for their best interest, bypassing the existing hardness to design a PoL mechanism with computational efficiency, a provable incentive-security guarantee and controllable difficulty.
• Score: 15.219480512237682
• License:
• Abstract: Most concurrent blockchain systems rely heavily on the Proof-of-Work (PoW) or Proof-of-Stake (PoS) mechanisms for decentralized consensus and security assurance. However, the substantial energy expenditure stemming from computationally intensive yet meaningless tasks has raised considerable concerns surrounding traditional PoW approaches, The PoS mechanism, while free of energy consumption, is subject to security and economic issues. Addressing these issues, the paradigm of Proof-of-Useful-Work (PoUW) seeks to employ challenges of practical significance as PoW, thereby imbuing energy consumption with tangible value. While previous efforts in Proof of Learning (PoL) explored the utilization of deep learning model training SGD tasks as PoUW challenges, recent research has revealed its vulnerabilities to adversarial attacks and the theoretical hardness in crafting a byzantine-secure PoL mechanism. In this paper, we introduce the concept of incentive-security that incentivizes rational provers to behave honestly for their best interest, bypassing the existing hardness to design a PoL mechanism with computational efficiency, a provable incentive-security guarantee and controllable difficulty. Particularly, our work is secure against two attacks, and also improves the computational overhead from $\Theta(1)$ to $O(\frac{\log E}{E})$. Furthermore, while most recent research assumes trusted problem providers and verifiers, our design also guarantees frontend incentive-security even when problem providers are untrusted, and verifier incentive-security that bypasses the Verifier's Dilemma. By incorporating ML training into blockchain consensus mechanisms with provable guarantees, our research not only proposes an eco-friendly solution to blockchain systems, but also provides a proposal for a completely decentralized computing power market in the new AI age.

Related papers

• Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions [0.0]
  Decentralized Finance (DeFi) has introduced significant security risks, including the proliferation of illicit accounts. Traditional detection methods are limited by the scarcity of labeled data and the evolving tactics of malicious actors. We propose a novel Self-Learning Ensemble-based Illicit account Detection framework to address these challenges.
  arXiv  Detail & Related papers  (2024-12-03T12:03:13Z)
• Collaborative Proof-of-Work: A Secure Dynamic Approach to Fair and Efficient Blockchain Mining [0.1759252234439348]
  This paper introduces a novel Collaborative Proof-of-Work (CPoW) mining approach designed to enhance efficiency and fairness in the network. By addressing the centralization and energy inefficiencies of traditional mining, this research contributes to a more sustainable blockchain ecosystem.
  arXiv  Detail & Related papers  (2024-12-01T05:59:27Z)
• Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
  We propose Authenticated Cyclic Redundancy Integrity Check (ACRIC) ACRIC preserves backward compatibility without requiring additional hardware and is protocol agnostic. We show that ACRIC offers robust security with minimal transmission overhead ( 1 ms)
  arXiv  Detail & Related papers  (2024-11-21T18:26:05Z)
• Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus Algorithm [2.5203968759841158]
  We propose Proof-of-Collaborative-Learning (PoCL), a multi-winner federated learning validated consensus mechanism. PoCL redirects the power of blockchains to train federated learning models. We present a novel evaluation mechanism to ensure the efficiency of the locally trained models of miners.
  arXiv  Detail & Related papers  (2024-07-17T21:14:05Z)
• It Takes Two: A Peer-Prediction Solution for Blockchain Verifier's Dilemma [12.663727952216476]
  We develop a Byzantine-robust peer prediction framework towards the design of one-phase Bayesian truthful mechanisms for the decentralized verification games. Our study provides a framework of incentive design for decentralized verification protocols that enhances the security and robustness of the blockchain.
  arXiv  Detail & Related papers  (2024-06-03T21:21:17Z)
• A Novel Endorsement Protocol to Secure BFT-Based Consensus in Permissionless Blockchain [1.3723120574076126]
  BFT-based consensus mechanisms are widely adopted in the permissioned blockchain to meet the high scalability requirements of the network. Sybil attacks are one of the most potential threats when applying BFT-based consensus mechanisms in permissionless blockchain. This paper presents a novel endorsement-based bootstrapping protocol with a signature algorithm that offers a streamlined, scalable identity endorsement and verification process.
  arXiv  Detail & Related papers  (2024-05-04T03:00:33Z)
• 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 Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
  The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure. This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus. We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
  arXiv  Detail & Related papers  (2024-01-19T14:52:04Z)
• Regulation conform DLT-operable payment adapter based on trustless - justified trust combined generalized state channels [77.34726150561087]
  Economy of Things (EoT) will be based on software agents running on peer-to-peer trustless networks. We give an overview of current solutions that differ in their fundamental values and technological possibilities. We propose to combine the strengths of the crypto based, decentralized trustless elements with established and well regulated means of payment.
  arXiv  Detail & Related papers  (2020-07-03T10:45:55Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)

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.