arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2404.09005cs.CRcs.AIcs.ETcs.GTcs.LG

具有激励安全性的学习证明

Proof-of-Learning with Incentive Security

National University of Singapore(新加坡国立大学);Tsinghua University(清华大学);Shanghai Qi Zhi Institute(上海期智研究院);HKUST (GZ)(香港科技大学(广州));New York University(纽约大学);Yau Mathematical Sciences Center(丘成桐数学科学中心)

Zishuo Zhao, Zhixuan Fang, Xuechao Wang, Xi Chen, Hongxu Su, Haibo Xiao, Yuan Zhou

AI总结:

针对PoW能耗与PoS安全问题及PoL易受攻击、难实现拜占庭安全的缺陷,本文提出激励安全性概念,设计高效、难度可控且可证明激励安全的学习证明机制,可抵御两类攻击并降低开销。

摘要:

大多数并发区块链系统严重依赖工作量证明(PoW)或权益证明(PoS)机制来实现去中心化共识和安全保障。然而,计算密集但无意义的任务所产生的大量能源消耗,引发了人们对传统PoW方法的大量担忧;PoS机制虽然不消耗能源,却存在安全和经济问题。为解决这些问题,有用工作证明(PoUW)范式试图将具有实际意义的挑战用作PoW,从而赋予能源消耗以切实价值。尽管此前在学习证明(PoL)方面的工作探索了将深度学习模型训练SGD任务用作PoUW挑战,但近期研究揭示了其易受对抗性攻击的脆弱性,以及构建拜占庭安全PoL机制在理论上的困难。本文提出激励安全性概念,通过激励理性证明者为自身最大利益而诚实行事,绕过上述既有困难,设计出一种兼具计算效率、可证明激励安全保证和可控难度的PoL机制。具体而言,我们的工作可抵御两种攻击,并将计算开销从Θ(1)改进至O(log E/E)。此外,尽管多数最新研究假设问题提供者和验证者可信,我们的设计即使在问题提供者不可信时也能保证前端激励安全性,并保证绕过验证者困境的验证者激励安全性。通过将机器学习训练纳入具有可证明保证的区块链共识机制,本研究不仅为区块链系统提出了一种生态友好的解决方案,也为全新AI时代的完全去中心化算力市场提供了一项方案。

英文摘要:

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 $Θ(1)$ 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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