BFFDT: Blockchain-Based Fair and Fine-Grained Data Trading Using Proxy Re-Encryption and Verifiable Commitment

Yangyang Long, Changgen Peng, Yuling Chen, Weijie Tan, Jing Sun

Abstract: Fair data trading is a complex process that is often hindered by a fundamental issue of trust between data suppliers and collectors. This mistrust can lead to an impasse: data collectors hesitate to pay upfront without the data in hand, while data suppliers hold back the data until they are assured of payment. Though enlisting a trusted third party may mitigate these issues, it also presents distinct security challenges that must be carefully considered. Observing that the blockchain technique has great potential to improve security, efficiency, and transparency of data trading, we present a blockchain-based fair data trading scheme, called BFFDT, which allows the data seller trade its data in part with an interested purchaser through a smart contract for revenue. In BFFDT, the data publisher first generates the authenticated tags based on the data fields and corresponding attribute values, then encrypts the corresponding attribute values individually and generates a dynamic Merkle hash tree (D-MHT) to ensure the consistency of the attributes and attribute values. In addition, we design an innovative pairing-based proxy re-encryption mechanism to transmit the ciphertext of a symmetric key to the purchaser’s public key via a re-encryption key without any third-party intermediary, and verifies the re-encryption key using the verifiable commitment. Furthermore, the BFFDT is formally proven to be secure against the deceitful actions of both the fraudulent seller and buyer, and the experimental outcomes further confirm that BFFDT offers high efficiency and practical applicability.