arXiv subjects

Yuanhaur Chang

Publications and source records attributed to Yuanhaur Chang.

2 recordsLinked to original sources

arXiv2025

Mind the Gap: Revealing Security Barriers through Situational Awareness of Small and Medium Business Key Decision-Makers

Key decision-makers in small and medium businesses (SMBs) often lack the awareness and knowledge to implement cybersecurity measures effectively. To gain a deeper understanding of how SMB executives navigate cybersecurity decision-making, we deployed a mixed-method approach, conducting semi-structured interviews (n=21) and online surveys (n=322) with SMB key decision-makers. Using thematic analysis, we revealed SMB decision-makers' perceived risks in terms of the digital assets they valued, and found reasons for their choice of defense measures and factors impacting security perception. We employed the situational awareness model to characterize decision-makers based on cybersecurity awareness, identifying those who have comparatively low awareness in the fight against adversaries. We further explored the relationship between awareness and business attributes, and constructed a holistic structural equation model to understand how awareness can be improved. Finally, we proposed interventions to help SMBs overcome potential challenges.

cs.CR↗

arXiv2025

SoK: Security and Privacy Risks of Healthcare AI

The integration of artificial intelligence (AI) and machine learning (ML) into healthcare systems holds great promise for enhancing patient care and care delivery efficiency; however, it also exposes sensitive data and system integrity to potential cyberattacks. Current security and privacy (S&P) research on healthcare AI is highly unbalanced in terms of healthcare deployment scenarios and threat models, and has a disconnected focus with the biomedical research community. This hinders a comprehensive understanding of the risks that healthcare AI entails. To address this gap, this paper takes a thorough examination of existing healthcare AI S&P research, providing a unified framework that allows the identification of under-explored areas. Our survey presents a systematic overview of healthcare AI attacks and defenses, and points out challenges and research opportunities for each AI-driven healthcare application domain. Through our experimental analysis of different threat models and feasibility studies on under-explored adversarial attacks, we provide compelling insights into the pressing need for cybersecurity research in the rapidly evolving field of healthcare AI.

cs.CR↗