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

doi:10.1109/BigData47090.2019.9005600

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

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Citations (Scopus)

22 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 language	English
Title of host publication	Proceedings 2019 IEEE International Conference on Big Data
Publisher	IEEE Computer Society
Pages	2726-2735
Number of pages	10
ISBN (Electronic)	9781728108582
ISBN (Print)	9781728108599
DOIs	https://doi.org/10.1109/BigData47090.2019.9005600
Publication status	Published - 24 Feb 2020
Event	IEEE Big Data 2019 - Los Angeles, United States Duration: 9 Dec 2019 → 12 Dec 2019 http://bigdataieee.org/BigData2019/

Publication series

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

Conference

Conference	IEEE Big Data 2019
Country/Territory	United States
City	Los Angeles
Period	9/12/19 → 12/12/19
Internet address	http://bigdataieee.org/BigData2019/

Keywords

Medical data
Smart healthcare
Data sharing
Privacy
Security
Personalised medicine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BigData47090.2019.9005600

0000-SERUMS-IEEE-Big-Data
Copyright © 2020 IEEE. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/BigData47090.2019.9005600
Accepted author manuscript, 4.62 MB

SERUMS: SERUMS
Kuster Filipe Bowles, J. (PI)
European Commission
1/01/19 → 30/06/22
Project: Standard

Juliana Kuster Filipe Bowles
- jkfbst-andrews.acuk
- School of Computer Science - Professor
Person: Academic

Cite this

Janjic, V., Kuster Filipe Bowles, J., Vermeulen, A. F., Silvina, A., Belk, M., Fidas, C., Pitsillides, A., Kumar, M., Rossborry, M., Vinov, M., Given-Wilson, T., Legay, A., Blackledge, E., Arredouani, R., Stylianou, G., & Huang, W. (2020). The SERUMS tool-chain: ensuring security and privacy of medical data in smart patient-centric healthcare systems. In Proceedings 2019 IEEE International Conference on Big Data (pp. 2726-2735). Article 9005600 (IEEE International Conference on Big Data). IEEE Computer Society. https://doi.org/10.1109/BigData47090.2019.9005600

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

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.",

keywords = "Medical data, Smart healthcare, Data sharing, Privacy, Security, Personalised medicine",

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

note = "Funding: EU H2020 project Serums: Securing Medical Data in Smart Patient-Centric Healthcare Systems (grant code: 826278).; IEEE Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

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doi = "10.1109/BigData47090.2019.9005600",

language = "English",

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Janjic, V, Kuster Filipe Bowles, J, Vermeulen, AF, Silvina, A, Belk, M, Fidas, C, Pitsillides, A, Kumar, M, Rossborry, M, Vinov, M, Given-Wilson, T, Legay, A, Blackledge, E, Arredouani, R, Stylianou, G & Huang, W 2020, The SERUMS tool-chain: ensuring security and privacy of medical data in smart patient-centric healthcare systems. in Proceedings 2019 IEEE International Conference on Big Data., 9005600, IEEE International Conference on Big Data, IEEE Computer Society, pp. 2726-2735, IEEE Big Data 2019, Los Angeles, California, United States, 9/12/19. https://doi.org/10.1109/BigData47090.2019.9005600

The SERUMS tool-chain: ensuring security and privacy of medical data in smart patient-centric healthcare systems. / Janjic, Vladimir; Kuster Filipe Bowles, Juliana; Vermeulen, Andreas Francois et al.
Proceedings 2019 IEEE International Conference on Big Data. IEEE Computer Society, 2020. p. 2726-2735 9005600 (IEEE International Conference on Big Data).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - The SERUMS tool-chain

T2 - IEEE Big Data 2019

AU - Janjic, Vladimir

AU - Kuster Filipe Bowles, Juliana

AU - Vermeulen, Andreas Francois

AU - Silvina, Agastya

AU - Belk, Marios

AU - Fidas, Christos

AU - Pitsillides, Andreas

AU - Kumar, Mohit

AU - Rossborry, Michael

AU - Vinov, Michael

AU - Given-Wilson, Thomas

AU - Legay, Axel

AU - Blackledge, Euan

AU - Arredouani, Rachid

AU - Stylianou, Georgios

AU - Huang, Wanting

N1 - Funding: EU H2020 project Serums: Securing Medical Data in Smart Patient-Centric Healthcare Systems (grant code: 826278).

PY - 2020/2/24

Y1 - 2020/2/24

N2 - 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.

AB - 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.

KW - Medical data

KW - Smart healthcare

KW - Data sharing

KW - Privacy

KW - Security

KW - Personalised medicine

U2 - 10.1109/BigData47090.2019.9005600

DO - 10.1109/BigData47090.2019.9005600

M3 - Conference contribution

SN - 9781728108599

T3 - IEEE International Conference on Big Data

SP - 2726

EP - 2735

BT - Proceedings 2019 IEEE International Conference on Big Data

PB - IEEE Computer Society

Y2 - 9 December 2019 through 12 December 2019

ER -

Janjic V, Kuster Filipe Bowles J, Vermeulen AF, Silvina A, Belk M, Fidas C et al. The SERUMS tool-chain: ensuring security and privacy of medical data in smart patient-centric healthcare systems. In Proceedings 2019 IEEE International Conference on Big Data. IEEE Computer Society. 2020. p. 2726-2735. 9005600. (IEEE International Conference on Big Data). doi: 10.1109/BigData47090.2019.9005600

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

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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Projects

SERUMS: SERUMS

Profiles

Juliana Kuster Filipe Bowles

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