# Distributed Data Vending on Blockchain

## TL;DR

This paper proposes a blockchain-based framework for secure distributed data vending that balances data retrieval effectiveness with leakage risk, demonstrated through electronic medical records.

## Contribution

It introduces a novel combination of data embedding and similarity learning for secure data exchange on blockchain, addressing key trade-offs.

## Key findings

- Framework effectively balances data retrieval and leakage risk
- Empirical results validate the framework's effectiveness
- Application demonstrated with electronic medical records

## Abstract

Recent advances in blockchain technologies have provided exciting opportunities for decentralized applications. Specifically, blockchain-based smart contracts enable credible transactions without authorized third parties. The attractive properties of smart contracts facilitate distributed data vending, allowing for proprietary data to be securely exchanged on a blockchain. Distributed data vending can transform domains such as healthcare by encouraging data distribution from owners and enabling large-scale data aggregation. However, one key challenge in distributed data vending is the trade-off dilemma between the effectiveness of data retrieval, and the leakage risk from indexing the data. In this paper, we propose a framework for distributed data vending through a combination of data embedding and similarity learning. We illustrate our framework through a practical scenario of distributing and aggregating electronic medical records on a blockchain. Extensive empirical results demonstrate the effectiveness of our framework.