Blockchain-based Privacy-Preserving Data Trading Using NFT and GANs

Abstract

The increasing value of data as a digital asset has motivated research on secure and privacy-preserving data trading frameworks. Traditional data exchange models expose raw datasets directly, leading to privacy leakage and unclear ownership attribution. This paper presents a blockchain-based data trading framework that integrates Non-Fungible Tokens (NFTs) for ownership verification and Generative Adversarial Networks (GANs) for privacy-preserving synthetic data generation. By leveraging differential privacy during GAN training, the framework ensures data usability while providing provable privacy guarantees. Experimental results on benchmark datasets demonstrate that the proposed model achieves a favorable trade-off between privacy and utility, supporting secure and efficient data circulation in decentralized environments.