We present the Differentially Private Blockchain-Based Vertical Federal Learning (DP-BBVFL) algorithm that provides verifiability and privacy guarantees for decentralized applications. DP-BBVFL uses a smart contract to aggregate the feature representations, i.e., the embeddings, from clients transparently. We apply local differential privacy to provide privacy for embeddings stored on a blockchain, hence protecting the original data. We provide the first prototype application of differential privacy with blockchain for vertical federated learning. Our experiments with medical data show that DP-BBVFL achieves high accuracy with a tradeoff in training time due to on-chain aggregation. This innovative fusion of differential privacy and blockchain technology in DP-BBVFL could herald a new era of collaborative and trustworthy machine learning applications across several decentralized application domains.

翻译：我们提出了差分隐私区块链纵向联邦学习算法，为去中心化应用提供可验证性与隐私保障。该算法通过智能合约透明地聚合来自客户端的特征表示。我们应用本地差分隐私技术为存储在区块链上的嵌入向量提供隐私保护，从而保护原始数据。本研究首次实现了差分隐私与区块链技术在纵向联邦学习中的原型应用。基于医疗数据的实验表明，该算法在保证高精度的同时，因链上聚合操作而需权衡训练时间。这种差分隐私与区块链技术的创新融合，可能为多个去中心化应用领域开启协作可信机器学习应用的新纪元。