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Big Data Analytics in Healthcare Applications: Privacy Implications for Individuals and Groups and Mitigation Strategies

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Information Systems (EMCIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 402))

Abstract

Big data analytics in healthcare present a potentially powerful means for addressing public health emergencies such as the COVID-19 pandemic. A challenging issue for health data to be used, however, is the protection of privacy. Research on big data privacy, especially in relation to healthcare, is still at an early stage and there is a lack of guidelines or best practice strategies for big data privacy protection. Moreover, while academic discourse focuses on individual privacy, research evidence shows that there are cases such as mass surveillance through sensing and other IoT technologies where the privacy of groups needs also to be considered. This paper explores these challenges, focusing on health data analytics; we identify and analyse privacy threats and implications for individuals and groups and we evaluate recent privacy preserving techniques for contact tracing.

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Authors and Affiliations

  1. Department of Information and Communication Systems Engineering, University of the Aegean, 83200, Karlovassi, Samos, Greece

    Paola Mavriki & Maria Karyda

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  1. Paola Mavriki
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  2. Maria Karyda
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Correspondence to Paola Mavriki or Maria Karyda .

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Editors and Affiliations

  1. Department of Digital Innovation, School of Business, University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  2. British University in Dubai, Dubai, United Arab Emirates

    Maria Papadaki

  3. School of Strategy and Leadership, Coventry University, Coventry, UK

    Muhammad Mustafa Kamal

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Mavriki, P., Karyda, M. (2020). Big Data Analytics in Healthcare Applications: Privacy Implications for Individuals and Groups and Mitigation Strategies. In: Themistocleous, M., Papadaki, M., Kamal, M.M. (eds) Information Systems. EMCIS 2020. Lecture Notes in Business Information Processing, vol 402. Springer, Cham. https://doi.org/10.1007/978-3-030-63396-7_35

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