13
views
0
recommends
+1 Recommend
2 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      More Than Just Privacy: Using Contextual Integrity to Evaluate the Long-Term Risks from COVID-19 Surveillance Technologies

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The global coronavirus pandemic has raised important questions regarding how to balance public health concerns with privacy protections for individual citizens. In this essay, we evaluate contact tracing apps, which have been offered as a technological solution to minimize the spread of COVID-19. We argue that apps such as those built on Google and Apple’s “exposure notification system” should be evaluated in terms of the contextual integrity of information flows; in other words, the appropriateness of sharing health and location data will be contextually dependent on factors such as who will have access to data, as well as the transmission principles underlying data transfer. We also consider the role of prevailing social and political values in this assessment, including the large-scale social benefits that can be obtained through such information sharing. However, caution should be taken in violating contextual integrity, even in the case of a pandemic, because it risks a long-term loss of autonomy and growing function creep for surveillance and monitoring technologies.

          Related collections

          Most cited references14

          • Record: found
          • Abstract: not found
          • Article: not found

          Privacy as contextual integrity

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            A Model of the 2014 Ebola Epidemic in West Africa with Contact Tracing

            A differential equations model is developed for the 2014 Ebola epidemics in Sierra Leone and Liberia. The model describes the dynamic interactions of the susceptible and infected populations of these countries. The model incorporates the principle features of contact tracing, namely, the number of contacts per identified infectious case, the likelihood that a traced contact is infectious, and the efficiency of the contact tracing process. The model is first fitted to current cumulative reported case data in each country. The data fitted simulations are then projected forward in time, with varying parameter regimes corresponding to contact tracing efficiencies. These projections quantify the importance of the identification, isolation, and contact tracing processes for containment of the epidemics.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              ‘There’s nothing really they can do with this information’: unpacking how users manage privacy boundaries for personal fitness information

                Bookmark

                Author and article information

                Journal
                Soc Media Soc
                Soc Media Soc
                SMS
                spsms
                Social Media + Society
                SAGE Publications (Sage UK: London, England )
                2056-3051
                30 July 2020
                July 2020
                : 6
                : 3
                : 2056305120948250
                Affiliations
                [1 ]University of Maryland, USA
                [2 ]Marquette University, USA
                Author notes
                [*]Jessica Vitak, University of Maryland, 4130 Campus Drive, College Park, MD 20742, USA. Email: jvitak@ 123456umd.edu
                Author information
                https://orcid.org/0000-0001-9362-9032
                https://orcid.org/0000-0003-4229-4847
                Article
                10.1177_2056305120948250
                10.1177/2056305120948250
                7399567
                34192036
                1475bde0-361f-4979-b2f9-0d5ac79112ac
                © The Author(s) 2020

                This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                Categories
                2K: Covid19
                Custom metadata
                July-September 2020
                ts1

                privacy,contextual integrity,covid-19,contact tracing,surveillance

                Comments

                Comment on this article