A Framework for User Biometric Privacy Protection in UAV Delivery Systems with Edge Computing

The development of intelligent logistics increasingly leads the evolution of future supply chain technology. As an innovative technology in the field of logistics, Unmanned Aerial Vehicles (UAVs) provide efficient, fast and flexible solutions for transportation and delivery. However, the application of UAVs needs to ensure effective identity authentication and the security of the delivery process. Because biometric data (such as fingerprints, facial recognition, iris scans) is highly sensitive personal information. Once stolen or abused, it may lead to serious personal privacy disclosure problems. In this paper, we use differential privacy and diffusion models to implement secure face recognition and identity authentication in edge computing environments for address the privacy issues in UAV delivery. The UAVs collect the user's biometric data through edge computing nodes during delivery, and uses a diffusion model for secure transmission to protect user privacy. The edge computing node at the receiving end performs face recognition and authentication to ensure that only legitimate users can accept the delivery. Our study not only improves the accuracy of user identity authentication, but also protects the privacy of users.