A Differentially Private Blockchain-Based Approach for Vertical Federated Learning

TL;DR

This paper introduces DP-BBVFL, a novel approach combining differential privacy and blockchain technology to enhance privacy, verifiability, and accuracy in vertical federated learning, demonstrated with medical data.

Contribution

It presents the first prototype integrating differential privacy with blockchain for vertical federated learning, ensuring privacy and transparency in decentralized data collaborations.

Findings

High accuracy achieved with privacy guarantees

Effective on-chain aggregation with acceptable training time

Demonstrated on medical data with promising results

Abstract

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.