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A perspective on data sharing in digital food safety systems.
Qian, Chenhao; Liu, Yuhan; Barnett-Neefs, Cecil; Salgia, Sudeep; Serbetci, Omer; Adalja, Aaron; Acharya, Jayadev; Zhao, Qing; Ivanek, Renata; Wiedmann, Martin.
Afiliación
  • Qian C; Department of Food Science, Cornell University, Ithaca, NY, USA.
  • Liu Y; School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.
  • Barnett-Neefs C; Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY, USA.
  • Salgia S; School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.
  • Serbetci O; School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.
  • Adalja A; SC Johnson College of Business, Cornell University, Ithaca, NY, USA.
  • Acharya J; School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.
  • Zhao Q; School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.
  • Ivanek R; Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY, USA.
  • Wiedmann M; Department of Food Science, Cornell University, Ithaca, NY, USA.
Crit Rev Food Sci Nutr ; : 1-17, 2022 Jul 26.
Article en En | MEDLINE | ID: mdl-35880485
In this age of data, digital tools are widely promoted as having tremendous potential for enhancing food safety. However, the potential of these digital tools depends on the availability and quality of data, and a number of obstacles need to be overcome to achieve the goal of digitally enabled "smarter food safety" approaches. One key obstacle is that participants in the food system and in food safety often lack the willingness to share data, due to fears of data abuse, bad publicity, liability, and the need to keep certain data (e.g., human illness data) confidential. As these multifaceted concerns lead to tension between data utility and privacy, the solutions to these challenges need to be multifaceted. This review outlines the data needs in digital food safety systems, exemplified in different data categories and model types, and key concerns associated with sharing of food safety data, including confidentiality and privacy of shared data. To address the data privacy issue a combination of innovative strategies to protect privacy as well as legal protection against data abuse need to be pursued. Existing solutions for maximizing data utility, while not compromising data privacy, are discussed, most notably differential privacy and federated learning.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Crit Rev Food Sci Nutr Asunto de la revista: CIENCIAS DA NUTRICAO Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Crit Rev Food Sci Nutr Asunto de la revista: CIENCIAS DA NUTRICAO Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos