Abstract
Future-generation healthcare systems will be highly distributed, combining centralised hospital systems with decentralised home-, work- and environment-based monitoring and diagnostics systems. These will reduce costs and injuryrelated risks whilst both improving quality of service, and reducing the response time for diagnostics and treatments made available to patients. To make this vision possible, medical data must be accessed and shared over a variety of mediums including untrusted networks. In this paper, we present the design and initial implementation of the SERUMS tool-chain for accessing, storing, communicating and analysing highly confidential medical data in a safe, secure and privacy-preserving way. In addition, we describe a data fabrication framework for generating large volumes of synthetic but realistic data, that is used in the design and evaluation of the tool-chain. We demonstrate the present version of our technique on a use case derived from the Edinburgh Cancer Centre, NHS Lothian, where information about the effects of chemotherapy treatments on cancer patients is collected from different distributed databases, analysed and adapted to improve ongoing treatments.
| Original language | English |
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| Title of host publication | Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 |
| Editors | Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye |
| Publisher | IEEE |
| Pages | 2726-2735 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781728108582 |
| DOIs | |
| Publication status | Published - 24 Feb 2020 |
Publication series
| Name | Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 |
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Keywords
- Data Sharing
- Medical data
- Personalised Medicine
- Privacy
- Security
- Smart Healthcare
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Information Systems and Management
Access to Document
- Author Accepted Manuscript
© AAM 2019. This is the author’s version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of 2019 IEEE International Conference on Big Data (BigData 2019. [http://doi.acm.org/10.1145/nnnnnn.nnnnnn.]
