Blockchain-Based Fair Payment for ABE with Outsourced Decryption

Hong, Linjian; Zhang, Kai; Gong, Junqing; Qian, Haifeng

doi:10.1007/s12083-022-01406-4

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…In recent years, the big data transaction market has garnered significant attention from researchers. In data transactions, fair payment refers to the timely receipt of the agreed-upon dataset or compensation when both parties of the transaction comply with the transaction agreement in good faith [20]. Zhou et al [21] proposed a distributed data vending framework based on blockchain by combining data embedding and similarity learning.…”

Section: Fair Paymentmentioning

confidence: 99%

A Blockchain-Based Privacy-Preserving and Fair Data Transaction Model in IoT

Zhou,

Zhang,

Han

et al. 2023

Applied Sciences

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The rapid development of the Internet of Things (IoT) has resulted in vast amounts of widely distributed data. Sharing these data can spur innovative advancements and enhance service quality. However, conventional data-sharing methods often involve third-party intermediaries, posing risks of single-point failures and privacy leaks. Moreover, these traditional sharing methods lack a secure transaction model to compensate for data sharing, which makes ensuring fair payment between data consumers and providers challenging. Blockchain, as a decentralized, secure, and trustworthy distributed ledger, offers a novel solution for data sharing. Nevertheless, since all nodes on the blockchain can access on-chain data, data privacy is inadequately protected, and traditional privacy-preserving methods like anonymization and generalization are ineffective against attackers with background knowledge. To address these issues, this paper proposes a decentralized, privacy-preserving, and fair data transaction model based on blockchain technology. We designed an adaptive local differential privacy algorithm, MDLDP, to protect the privacy of transaction data. Concurrently, verifiable encrypted signatures are employed to address the issue of fair payment during the data transaction process. This model proposes a committee structure to replace the individual arbitrator commonly seen in traditional verifiable encrypted signatures, thereby reducing potential collusion between dishonest traders and the arbitrator. The arbitration committee leverages threshold signature techniques to manage arbitration private keys. A full arbitration private key can only be collaboratively constructed by any arbitrary t members, ensuring the key’s security. Theoretical analyses and experimental results reveal that, in comparison to existing approaches, our model delivers enhanced transactional security. Moreover, while guaranteeing data availability, MDLDP affords elevated privacy protection.

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Section: Fair Paymentmentioning

confidence: 99%