The SERUMS tool-chain: Ensuring Security and Privacy of Medical Data in Smart Patient-Centric Healthcare Systems
| Autor: | Janjic, V., Bowles, J.K.F., Vermeulen, A.F., Silvina, A., Belk, M., Fidas, C., Pitsillides, Andreas, Kumar, M., Rossbory, M., Vinov, M., Given-Wilson, T., Legay, A., Blackledge, E., Arredouani, R., Stylianou, G., Huang, W. |
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| Přispěvatelé: | Pitsillides, Andreas [0000-0001-5072-2851], European Commission, University of St Andrews. School of Computer Science, University of St Andrews.School of Computer Science, UCL - SST/ICTM/INGI - Pôle en ingénierie informatique |
| Rok vydání: | 2019 |
| Předmět: |
Healthcare system
QA75 Artificial intelligence Computer science QA75 Electronic computers. Computer science NDAS Medical data 02 engineering and technology Computer security computer.software_genre Data science RC0254 Quality of service SDG 3 - Good Health and Well-being Machine learning 0202 electrical engineering electronic engineering information engineering Distributed database 0501 psychology and cognitive sciences Confidentiality 050107 human factors RC0254 Neoplasms. Tumors. Oncology (including Cancer) cyber security 05 social sciences 020207 software engineering Smart healthcare 3. Good health Variety (cybernetics) Data sharing Patient centric Privacy Security Personalised medicine computer |
| Zdroj: | 2019 IEEE International Conference on Big Data (Big Data) St Andrews Research Repository Dépôt Institutionel de l’Université catholique de Louvain et de l’Université Saint-Louis UnpayWall ORCID Microsoft Academic Graph IEEE BigData |
| Popis: | Funding: EU H2020 project Serums: Securing Medical Data in Smart Patient-Centric Healthcare Systems (grant code: 826278). 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. Postprint |
| Databáze: | OpenAIRE |
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