@inproceedings{0a75f2971bf54fa0a74957f7336793e2,
title = "Intellectual Property Data Trading Through NFTization",
abstract = "Intellectual Property (IP) is a special type of data that has broad and high trading demands. Existing blockchain-based IP data trading schemes can promote the IP data trading market by removing the dependence on centralized platforms. However, the problem of trading fairness among sellers and buyers is more challenging compared to centralized approaches. This paper addresses the trading fairness problem by representing the data as Non-Fungible Tokens (NFTs) and separating usage rights and ownership. An NFTized IP data trading system is designed and a two-stage fair trading scheme is proposed. They ensure that buyers need not pay additional money if the IP content is not useful for them, and the sellers will not lose the IP ownership until they receive additional money in the second stage of trading. A prototype for the system is realized, and based on it, a set of experiments are carried out to evaluate the performance. The experimental results show the cost is acceptable.",
keywords = "blockchain, data trading, IP data, NFT, trading fairness",
author = "Ziyang Ji and Jie Zhang and Steven Guan and Man, \{Ka Lok\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2nd International Conference on Big Data and Privacy Computing, BDPC 2024 ; Conference date: 10-01-2024 Through 12-01-2024",
year = "2024",
month = jan,
doi = "10.1109/BDPC59998.2024.10649069",
language = "English",
series = "2024 2nd International Conference on Big Data and Privacy Computing, BDPC 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "179--185",
booktitle = "2024 2nd International Conference on Big Data and Privacy Computing, BDPC 2024",
}