Privacy, Security and Trust in Collaborative Models for Food Consumption Data Gathering

Food consumption data is vital to dietary exposure assessments. To facilitate such evaluation, the European Food Safety Authority has developed a centralised hub — the Comprehensive European Food Consumption Database — which contains individual records from roughly 100,000 Europeans. To date, data is mainly gathered from two sources: first, Member States are legally obligated to monitor and transmit consumption data to the EFSA; and second, the food industry and academia voluntarily submit dietary information in response to EFSA’s “call for data”. Among other goals, the EFSA Strategy 2020 not only prioritises the engagement of citizens in the risk assessment by promoting trust. Accordingly, in 2017 EFSA published a tender aimed at exploring collaborative data collection methods which rely on smartphone and mobile applications to collect consumption data, with the goal of increasing trust between consumers and the Authority. With the increasing popularity of mobile applications for the self-monitoring of users’ diets, consumers’ willingness to voluntarily share such personal information could be exploited to adopt the aforementioned methodologies. This paper investigates the convenience of such new solutions under a privacy-oriented methodology that analyses legal and ethical concerns raised by the adoption of crowdsourced and collaborative data collection methods. To analyse these emerging issues, this paper discusses the most prominent collaborative solutions from a technical perspective and identifies the extent to which consumption data may fall within the category of “health data” for the purposes of the EU General Data Protection Regulation; consequently, it raises specific concerns pertaining to biases in the data model and data gaps. Finally, the paper demonstrates that any increase in trust would bethwarted unless individual data protection, group privacy, and security of data are prioritised before the adoption of these collaborative methods.