@inproceedings{5e8dd9bd3eb0494baba080775da84257,
title = "A Blockchain-based Data Sharing Marketplace with a Federated Learning Use Case",
abstract = "Due to the sharp growth of employing mobile devices and IoT (Internet of Things) sensors in daily life, tremendous generated or collected data become one of the most valuable assets for not only users but also numerous applications, which provide various services using user data. However, a large portion of such data is possessed by only a few giant companies in a centralized manner. This incurs the concerns of how user data are harnessed and used and who can use such data because of many cases of privacy violence and data leakage. Therefore, in this paper, we propose a decentralized data sharing marketplace using Ethereum to enable users to share their data in a privacy-preserving and self-governing manner. Users can only share parts of the data from their devices they want to share in the marketplace and gain rewards from the bidding of buyers anonymously. Furthermore, a federated learning use case is demonstrated as a privacy-enhanced application of the proposed marketplace to encourage users to share processed data to avoid raw data leakage.",
keywords = "blockchain, data sharing, decentralized marketplace, federated learning",
author = "Zihan Zhou and Chenxiao Guo and Xiaoshuai Zhang and Ruiyu Wang and Lei Zhang and Muhammad Imran",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 5th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2023 ; Conference date: 01-05-2023 Through 05-05-2023",
year = "2023",
doi = "10.1109/ICBC56567.2023.10174981",
language = "English",
series = "2023 IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2023",
address = "United States",
}