Differential-Privacy-Based Collaborative Protection for Visual and Location Data in UAV Semantic Communications

Unmanned aerial vehicle semantic communications are increasingly required in low-altitude sensing, intelligent inspection, and emergency response, where raw image transmission is difficult to sustain under limited onboard resources and time-varying air-to-ground links. Meanwhile, the simultaneous transmission of visual semantic features and object-centre location metadata under third-party eavesdropping creates a dual-privacy vulnerability: an attacker can exploit both to reconstruct sensitive content. In this paper, we propose a differential privacy-based collaborative protection framework that inserts dedicated perturbations into visual semantic and location descriptors before transmission. For visual data, we design a region-aware differential privacy mechanism that applies stronger noise to sensitive semantic regions while preserving utility for non-critical areas. For location data, a scenario-adaptive strategy is developed, comprising randomized differential privacy for discrete grid-based location information (coarse spatial awareness) and Laplace-based differential privacy for continuous coordinates (fine-grained protection). To balance privacy and utility, we formulate a joint optimization problem. It maximizes legitimate-side semantic task performance by coordinating the visual privacy budget, location privacy budget, and transmit power. A BCD-based algorithm is developed to solve this non-convex problem. Attacker-side recoverability is verified empirically at the optimized operating point. Simulation results demonstrate stable convergence within a small number of iterations. Compared with uniform differential privacy, the proposed framework achieves a superior task-level privacy-utility trade-off and provides selective sensitive-region protection, with the two mechanisms yielding comparable whole-image attack suppression.