Privacy-Aware Intelligent Learning Under Adversarial, Biomedical, and Disaster Contexts

Abstract

Privacy-aware intelligent learning systems increasingly operate across security-sensitive, biomedical, and disaster-governance contexts. Universal and transferable adversarial attacks expose vulnerabilities in vision-language models, especially when surrogate models can generalize attacks across targets. Sequential recommendation with LLMs introduces another high-impact learning environment where dynamic indexing and user behavior modeling shape personalized decisions. Data governance architectures are therefore important for coordinating privacy, lifelong learning, and system adaptability. Biomedical evidence on hepatic ischemia-reperfusion and remote cardiac injury illustrates how intelligent systems may support complex mechanistic reasoning in health contexts. Flood relocation research adds a disaster-policy setting where intelligent learning systems must support heterogeneous household decisions and public-risk communication. Together, this literature cluster highlights the need for intelligent systems that are robust, privacy-aware, interpretable, and suitable for high-stakes environments.