Give more data, awareness and control to individual citizens, and they will help COVID-19 containment.

COVID-19 Contact tracing Mobility data analysis Personal data store

Journal

Ethics and information technology
ISSN: 1388-1957
Titre abrégé: Ethics Inf Technol
Pays: Netherlands
ID NLM: 101248311

Informations de publication

Date de publication:
2021
Historique:
medline: 9 2 2021
pubmed: 9 2 2021
entrez: 8 2 2021
Statut: ppublish

Résumé

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.

Identifiants

pubmed: 33551673
doi: 10.1007/s10676-020-09572-w
pii: 9572
pmc: PMC7851322
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1-6

Informations de copyright

© The Author(s) 2021.

Auteurs

Mirco Nanni (M)

ISTI-CNR, Pisa, Italy.

Gennady Andrienko (G)

IAIS-Fraunhofer, Sankt Augustin, Germany.
City University of London, London, UK.

Albert-László Barabási (AL)

Northeastern University, Boston, USA.

Chiara Boldrini (C)

IIT-CNR, Pisa, Italy.

Francesco Bonchi (F)

ISI Foundation, Turin, Italy.
Eurecat, Barcelona, Spain.

Ciro Cattuto (C)

ISI Foundation, Turin, Italy.
University of Torino, Turin, Italy.

Francesca Chiaromonte (F)

Sant'Anna School of Advanced Studies Pisa, Pisa, Italy.
Penn State University, State College, USA.

Giovanni Comandé (G)

Sant'Anna School of Advanced Studies Pisa, Pisa, Italy.

Marco Conti (M)

IIT-CNR, Pisa, Italy.

Mark Coté (M)

King's College London, London, UK.

Frank Dignum (F)

Umeå University, Umeå, Sweden.

Virginia Dignum (V)

Umeå University, Umeå, Sweden.

Josep Domingo-Ferrer (J)

Universitat Rovira i Vir-gili, Tarragona, Catalonia Spain.

Paolo Ferragina (P)

University of Pisa, Pisa, Italy.

Fosca Giannotti (F)

ISTI-CNR, Pisa, Italy.

Riccardo Guidotti (R)

University of Pisa, Pisa, Italy.

Dirk Helbing (D)

ETH Zurich, Zurich, Switzerland.

Kimmo Kaski (K)

Aalto University School of Science, Espoo, Finland.

Janos Kertesz (J)

Central European University, Budapest, Hungary.

Sune Lehmann (S)

Technical University of Denmark, Lyngby, Denmark.

Bruno Lepri (B)

FBK, Trento, Italy.

Paul Lukowicz (P)

DFKI, Kaiserslautern, Germany.

Stan Matwin (S)

Dalhousie University, Halifax, Canada.
Polish Academy of Sciences, Warsaw, Poland.

David Megías Jiménez (DM)

Universitat Oberta de Catalunya, Barcelona, Spain.

Anna Monreale (A)

University of Pisa, Pisa, Italy.

Katharina Morik (K)

TU Dortmund University, Dortmund, Germany.

Nuria Oliver (N)

ELLIS Alicante, Alicante, Spain.
Data-Pop Alliance, New York, USA.

Andrea Passarella (A)

IIT-CNR, Pisa, Italy.

Andrea Passerini (A)

Universita degli Studi di Trento, Trento, Italy.

Dino Pedreschi (D)

University of Pisa, Pisa, Italy.

Alex Pentland (A)

MIT, Cambridge, USA.

Fabio Pianesi (F)

EIT Digital, Povo, Italy.

Francesca Pratesi (F)

University of Pisa, Pisa, Italy.

Salvatore Rinzivillo (S)

ISTI-CNR, Pisa, Italy.

Salvatore Ruggieri (S)

University of Pisa, Pisa, Italy.

Arno Siebes (A)

Universiteit Utrecht, Utrecht, The Netherlands.

Vicenc Torra (V)

Umeå University, Umeå, Sweden.
Maynooth University, Maynooth, Ireland.

Roberto Trasarti (R)

ISTI-CNR, Pisa, Italy.

Jeroen van den Hoven (JVD)

TU Delft, Delft, The Netherlands.

Alessandro Vespignani (A)

Northeastern University, Boston, USA.

Classifications MeSH