Demo: Usage Control using Controlled Privacy Aware Face Recognition

Abstract

In this paper, we demonstrate an application of privacy-preserving face recognition combined with an Attribute-Based Access Control framework to regulate access from subjects to critical resources while preserving the subject's privacy. The demonstrator exploits a mechanism that dynamically computes the best trade-off between ensured privacy and data utility, based on image acquisition conditions, and a decision engine based on XACML policies to express complex and dynamic conditions. The demonstrator can handle the dynamic association of new identities, as well as modification of access conditions. Attendees of the demo session can interact with the demo in a variety of ways, including modifying the camera input, but also through the customization of rules as well as the privacy parameter.