  The highly transmissible COVID-19 disease is a serious threat to people's
health and life. To automate tracing those who have been in close physical
contact with newly infected people and/or to analyse tracing-related data,
researchers have proposed various ad-hoc programs that require being executed
on users' smartphones. Nevertheless, the existing solutions have two primary
limitations: (1) lack of generality: for each type of analytic task, a certain
kind of data needs to be sent to an analyst; (2) lack of transparency: parties
who provide data to an analyst are not necessarily infected individuals;
therefore, infected individuals' data can be shared with others (e.g., the
analyst) without their fine-grained and direct consent. In this work, we
present Glass-Vault, a protocol that addresses both limitations simultaneously.
It allows an analyst to run authorised programs over the collected data of
infectious users, without learning the input data. Glass-Vault relies on a new
variant of generic Functional Encryption that we propose in this work. This new
variant, called DD-Steel, offers these two additional properties: dynamic and
decentralised. We illustrate the security of both Glass-Vault and DD-Steel in
the Universal Composability setting. Glass-Vault is the first UC-secure
protocol that allows analysing the data of Exposure Notification users in a
privacy-preserving manner. As a sample application, we indicate how it can be
used to generate "infection heatmaps".
