@inproceedings{040a64183b4f480b82177d312c81c78a,
title = "Privacy-Preserving Framework to Facilitate Shared Data Access for Wearable Devices",
abstract = "Wearable devices are emerging as effective modalities for the collection of individuals' data. While this data can be leveraged for use in several areas ranging from health-care to crime investigation, storing and securely accessing such information while preserving privacy and detecting any tampering attempts are significant challenges. This paper describes a decentralized system that ensures an individual's privacy, maintains an immutable log of any data access, and provides decentralized access control management. Our proposed framework uses a custom permissioned blockchain protocol to securely log data transactions from wearable devices in the blockchain ledger. We have implemented a proof-of-concept for our framework, and our preliminary evaluation is summarized to demonstrate our proposed framework's capabilities. We have also discussed various application scenarios of our privacy-preserving model using blockchain and proof-of-authority. Our research aims to detect data tampering attempts in data sharing scenarios using a thorough transaction log model.",
keywords = "Blockchain, Health data sharing, Privacy, Security, Wearable Device",
author = "Dane Troyer and Justin Henry and Hoda Maleki and Gokila Dorai and Bethany Sumner and Gagan Agrawal and Jon Ingram",
note = "Funding Information: We would like to formally acknowledge the contributions of a few key individuals who contributed to the completion of this project. We thank Hunter Thomas, a former research assistant, for his efforts in laying the groundwork for our PPAM framework model. We would also like to thank Prof. Konstantin Busch, Dept. of Computer and Cyber Sciences at Augusta University, who provided insights into various consensus algorithms. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Big Data, Big Data 2021 ; Conference date: 15-12-2021 Through 18-12-2021",
year = "2021",
doi = "10.1109/BigData52589.2021.9671690",
language = "English (US)",
series = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2583--2592",
editor = "Yixin Chen and Heiko Ludwig and Yicheng Tu and Usama Fayyad and Xingquan Zhu and Hu, {Xiaohua Tony} and Suren Byna and Xiong Liu and Jianping Zhang and Shirui Pan and Vagelis Papalexakis and Jianwu Wang and Alfredo Cuzzocrea and Carlos Ordonez",
booktitle = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",
}
