TY - GEN
T1 - Patient controlled, privacy preserving IoT healthcare data sharing framework
AU - Chowdhury, Mohammad Jabed Morshed
AU - Kayes, A. S.M.
AU - Watters, Paul
AU - Scolyer-Gray, Patrick
AU - Ng, Alex
AU - Dillon, Tharam
N1 - Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2020
Y1 - 2020
N2 - Healthcare data personally collected by individuals with wearable devices have become important sources of information for healthcare professionals and medical research worldwide. User-Generated Data (UGD) offers unique and sometimes fine-grained insight into the lived experiences and medical conditions of patients. The sensitive subject-matter of medical data can facilitate the exploitation and/or control of victims. Data collection in medical research therefore restricts access control over participant-data to the researchers. Therefore, cultivating trust with prospective participants concerned about the security of their medical data presents formidable challenges. Anonymization can allay such concerns, but at the cost of information loss. Moreover, such techniques cannot necessarily be applied on real-time streaming health data. In this paper, we aim to analyze the technical requirements to enable individuals to share their real-time wearable healthcare data with researchers without compromising privacy. An extension for delay-free anonymization techniques for real-time streaming health data is also proposed.
AB - Healthcare data personally collected by individuals with wearable devices have become important sources of information for healthcare professionals and medical research worldwide. User-Generated Data (UGD) offers unique and sometimes fine-grained insight into the lived experiences and medical conditions of patients. The sensitive subject-matter of medical data can facilitate the exploitation and/or control of victims. Data collection in medical research therefore restricts access control over participant-data to the researchers. Therefore, cultivating trust with prospective participants concerned about the security of their medical data presents formidable challenges. Anonymization can allay such concerns, but at the cost of information loss. Moreover, such techniques cannot necessarily be applied on real-time streaming health data. In this paper, we aim to analyze the technical requirements to enable individuals to share their real-time wearable healthcare data with researchers without compromising privacy. An extension for delay-free anonymization techniques for real-time streaming health data is also proposed.
UR - http://www.scopus.com/inward/record.url?scp=85108155420&partnerID=8YFLogxK
U2 - 10.24251/HICSS.2020.453
DO - 10.24251/HICSS.2020.453
M3 - Conference proceeding contribution
AN - SCOPUS:85108155420
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 3700
EP - 3710
BT - Proceedings of the 53rd Annual Hawaii International Conference on System Sciences, HICSS 2020
A2 - Bui, Tung X.
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 53rd Annual Hawaii International Conference on System Sciences, HICSS 2020
Y2 - 7 January 2020 through 10 January 2020
ER -