Privacy-Preserving Data Analytics in Internet of Medical Things
Bakhtawar Mudassar,
Shahzaib Tahir,
Fawad Khan (),
Syed Aziz Shah,
Syed Ikram Shah and
Qammer Hussain Abbasi
Additional contact information
Bakhtawar Mudassar: Department of Information Security, College of Signals, National University of Sciences and Technology, H12, Islamabad 44000, Pakistan
Shahzaib Tahir: Department of Information Security, College of Signals, National University of Sciences and Technology, H12, Islamabad 44000, Pakistan
Fawad Khan: Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5RW, UK
Syed Aziz Shah: Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5RW, UK
Syed Ikram Shah: College of Electrical and Mechanical Engineering, National University of Sciences and Technology, H12, Islamabad 44000, Pakistan
Qammer Hussain Abbasi: School of Engineering, James Watt Building (South), University of Glasgow, Glasgow G12 8QQ, UK
Future Internet, 2024, vol. 16, issue 11, 1-30
Abstract:
The healthcare sector has changed dramatically in recent years due to depending more and more on big data to improve patient care, enhance or improve operational effectiveness, and forward medical research. Protecting patient privacy in the era of digital health records is a major challenge, as there could be a chance of privacy leakage during the process of collecting patient data. To overcome this issue, we propose a secure, privacy-preserving scheme for healthcare data to ensure maximum privacy of an individual while also maintaining their utility and allowing for the performance of queries based on sensitive attributes under differential privacy. We implemented differential privacy on two publicly available healthcare datasets, the Breast Cancer Prediction Dataset and the Nursing Home COVID-19 Dataset. Moreover, we examined the impact of varying privacy parameter ( ε ) values on both the privacy and utility of the data. A significant part of this study involved the selection of ε , which determines the degree of privacy protection. We also conducted a computational time comparison by performing multiple complex queries on these datasets to analyse the computational overhead introduced by differential privacy. The outcomes demonstrate that, despite a slight increase in query processing time, it remains within reasonable bounds, ensuring the practicality of differential privacy for real-time applications.
Keywords: differential privacy; healthcare data; data sharing; user privacy; data utility (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.mdpi.com/1999-5903/16/11/407/pdf (application/pdf)
https://www.mdpi.com/1999-5903/16/11/407/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:16:y:2024:i:11:p:407-:d:1514414
Access Statistics for this article
Future Internet is currently edited by Ms. Grace You
More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().