PrivEdge-VLM: Risk-Adaptive Privacy-Preserving Edge Vision–Language Analytics for UAV-Assisted Cyber–Physical–Social Systems

UAV-assisted cyber–physical–social systems increasingly use visual analytics for disaster response, infrastructure monitoring, traffic management, and safety-oriented situational awareness. Raw aerial imagery can expose faces, license plates, private-property context, location traces, and sensitive human activity, while cloud-based vision–language processing can increase latency, bandwidth use, and governance risk. This article presents PrivEdge-VLM, a risk-adaptive privacy-preserving edge vision–language analytics framework for UAV-assisted cyber–physical–social systems. PrivEdge-VLM integrates object detection, privacy-sensitive region detection, mission-aware semantic tokenization, local inference, sanitized split inference, federated LoRA adaptation with differential privacy and simulated masked aggregation, and deterministic operator guardrails. Across public UAV imagery, public privacy benchmarks, and non-human staged object protocol examples, the evaluation compares raw-cloud, downsampling, static-blur, local-VLM, raw-embedding split, federated static-privacy, no-DP, and full configurations. The full configuration preserves mission utility while reducing residual visual PII, plate-OCR leakage, membership-inference risk, embedding inversion, bandwidth, and unsafe identity-oriented responses under the stated attack protocols. The edge hardware deployment separates privacy-front-end throughput from event-level VLM query latency. The results indicate that UAV VLM privacy is a CPS co-design problem involving semantic transformation, attack-based leakage evaluation, resource-aware deployment, and operator oversight.