构建去中心化数据市场，实现隐私保护下的协作学习与激励兼容。
D2M: A Decentralized, Privacy-Preserving, Incentive-Compatible Data Marketplace for Collaborative Learning
- 通过区块链智能合约管理拍卖与争议，链下计算层执行训练任务。
- 在30%恶意节点下仍保持90%以上准确率，复杂数据集达56%准确率。
- 机制设计确保诚实行为为最优策略，适合真实场景的去中心化数据共享。
协同机器学习与数据分析日益增长的需求呼唤安全、去中心化的数据共享框架，以平衡隐私、信任与激励。现有方法如联邦学习依赖可信聚合方且缺乏拜占庭容错，区块链框架则面临计算密集型训练和激励整合难题。本文提出 prot，一个融合联邦学习、区块链仲裁与经济激励的去中心化数据市场。数据买家通过区块链智能合约提交竞标请求，系统负责拍卖、资金托管与争议解决。计算密集型训练由链下分布式执行层 cone（Compute Network for Execution）完成。为抵御恶意行为， prot 集成改进版 YODA 协议，采用指数增长执行集合实现鲁棒共识，并引入 Corrected OSMD 抵御卖家的恶意或低质贡献。所有协议均激励相容，博弈论分析表明诚实为占优策略。我们在 Ethereum 上实现 prot，基于 MNIST、Fashion-MNIST 与 CIFAR-10 在不同对抗环境下评估。结果显示， prot 在 MNIST 上达到最高 99% 准确率，Fashion-MNIST 达 90%，即使面对 30% 拜占庭节点也仅下降不足 3%，而 CIFAR-10 复杂数据集仍达 56% 准确率。结果表明， prot 能保障隐私，在对抗条件下保持稳健，并随参与方数量高效扩展，是现实世界去中心化数据共享的可行基础。
原文摘要 · Abstract (English)
The rising demand for collaborative machine learning and data analytics calls for secure and decentralized data sharing frameworks that balance privacy, trust, and incentives. Existing approaches, including federated learning (FL) and blockchain-based data markets, fall short: FL often depends on trusted aggregators and lacks Byzantine robustness, while blockchain frameworks struggle with computation-intensive training and incentive integration. We present \prot, a decentralized data marketplace that unifies federated learning, blockchain arbitration, and economic incentives into a single framework for privacy-preserving data sharing. \prot\ enables data buyers to submit bid-based requests via blockchain smart contracts, which manage auctions, escrow, and dispute resolution. Computationally intensive training is delegated to \cone\ (\uline{Co}mpute \uline{N}etwork for \uline{E}xecution), an off-chain distributed execution layer. To safeguard against adversarial behavior, \prot\ integrates a modified YODA protocol with exponentially growing execution sets for resilient consensus, and introduces Corrected OSMD to mitigate malicious or low-quality contributions from sellers. All protocols are incentive-compatible, and our game-theoretic analysis establishes honesty as the dominant strategy. We implement \prot\ on Ethereum and evaluate it over benchmark datasets -- MNIST, Fashion-MNIST, and CIFAR-10 -- under varying adversarial settings. \prot\ achieves up to 99\% accuracy on MNIST and 90\% on Fashion-MNIST, with less than 3\% degradation up to 30\% Byzantine nodes, and 56\% accuracy on CIFAR-10 despite its complexity. Our results show that \prot\ ensures privacy, maintains robustness under adversarial conditions, and scales efficiently with the number of participants, making it a practical foundation for real-world decentralized data sharing.
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