The SERUMS tool-chain: ensuring security and privacy of medical data in smart patient-centric healthcare systems

Vladimir Janjic, Juliana Kuster Filipe Bowles, Andreas Francois Vermeulen, Agastya Silvina, Marios Belk, Christos Fidas, Andreas Pitsillides, Mohit Kumar, Michael Rossborry, Michael Vinov, Thomas Given-Wilson, Axel Legay, Euan Blackledge, Rachid Arredouani, Georgios Stylianou, Wanting Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
10 Downloads (Pure)

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 injury-related 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, NHSLothian, 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 languageEnglish
Title of host publicationProceedings 2019 IEEE International Conference on Big Data
PublisherIEEE Computer Society
Pages2726-2735
Number of pages10
ISBN (Electronic)9781728108582
ISBN (Print)9781728108599
DOIs
Publication statusPublished - 24 Feb 2020
EventIEEE Big Data 2019 - Los Angeles, United States
Duration: 9 Dec 201912 Dec 2019
http://bigdataieee.org/BigData2019/

Publication series

NameIEEE International Conference on Big Data
ISSN (Print)2639-1589

Conference

ConferenceIEEE Big Data 2019
Country/TerritoryUnited States
CityLos Angeles
Period9/12/1912/12/19
Internet address

Keywords

  • Medical data
  • Smart healthcare
  • Data sharing
  • Privacy
  • Security
  • Personalised medicine

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