On Monetizing Personal Wearable Devices Data: A Blockchain-based Marketplace for Data Crowdsourcing and Federated Machine Learning in Healthcare

Authors

  • Mohamed Emish Department of Informatics University of California, Irvine, USA
  • Hari Kishore Chaparala Department of Computer Science University of California, Irvine, USA
  • Zeyad Kelani Department of InformaticsUniversity of California, Irvine, USA Political Science DepartmentFaculty of Economics and Political ScienceCairo University, Egypt
  • Sean D. Young Department of Informatics University of California, Irvine, USA Department of Emergency Medicine University of California, Irvine, USA

DOI:

https://doi.org/10.30564/aia.v4i2.5316

Abstract

Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights. While wearable device data helps to monitor, detect, and predict diseases and health conditions, some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns. Moreover, wearable devices have been recently available as commercial products; thus large, diverse, and representative datasets are not available to most researchers. In this article, we propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers (e.g., researchers) to make wearable device data more available to healthcare researchers. To secure the data transactions in a privacy-preserving manner, we use a decentralized approach using Blockchain and Non-Fungible Tokens (NFTs). To ensure data originality and integrity with secure validation, our marketplace uses Trusted Execution Environments (TEE) in wearable devices to verify the correctness of health data. The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share. To ensure user participation, we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs. We also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits. If widely adopted, we believe that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives. 

Keywords:

Wearable devices, Data Integrity, Data Validation, Federated Learning, Blockchain, Trusted Execution Environment, Health Informatics, Healthcare data collection, Data monetization

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How to Cite

Emish, M., Chaparala, H. K., Kelani, Z., & Young, S. D. (2023). On Monetizing Personal Wearable Devices Data: A Blockchain-based Marketplace for Data Crowdsourcing and Federated Machine Learning in Healthcare. Artificial Intelligence Advances, 4(2). https://doi.org/10.30564/aia.v4i2.5316

Issue

Vol. 4 , Iss. 2 (October 2022)

Article Type

Articles

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Copyright © 2023 Mohamed Emish, Hari Kishore Chaparala, Zeyad Kelani, Sean D. Young

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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