Assessment method for low-altitude UAV-induced privacy invasion risk based on risk evolution process

Against the backdrop of increasingly prominent privacy invasion risks posed by unmanned aerial vehicles (UAVs), this study proposes an innovative risk assessment method for UAV-induced privacy invasion, aiming to provide theoretical support for the safety management and privacy protection of low-altitude UAVs. Based on the risk evolution process for UAV-induced privacy invasion, the method adopts a dual-dimensional evaluation system: assessing the severity level of undesired incidents by quantifying the significance of protected individuals through analyzing their criticality within social organizations; and dynamically calculating the probability of visible privacy information leakage for protected individuals by incorporating variables such as UAV tracking duration, image capture quality, and environmental factors, thereby enabling real-time evaluation of occurrence probability level of hazards in risk scenarios. By integrating these two assessment results, the privacy invasion risk level is ultimately determined based on a risk matrix. The assessment system also incorporates an implementation framework for deep learning networks to predict identification probability and information clarity in real time. To validate the method's effectiveness, two experimental scenarios were designed: a virtual organizational scenario and a UAV tracking scenario. Experimental validation demonstrates that the proposed framework not only accurately quantifies the importance of protected individuals but also dynamically captures the evolving risk levels influenced by factors such as image capture quality and cumulative surveillance duration. The research outcomes can serve as a real-time risk assessment tool for regulatory authorities and provide a basis for formulating privacy protection measures.