Cybersecurity and Privacy Governance in IoT-Enabled Social Work: A Systematic Review and Risk Framework - Chen, Yih-Chang; Lin, Chia-Ching (Buletin Ilmiah Sarjana Teknik Elektro, 2025)
Social work practice is rapidly integrating Internet of Things (IoT) technologies to expand service delivery, yet this integration introduces significant cybersecurity and privacy vulnerabilities that disproportionately threaten vulnerable populations. Existing literature predominantly emphasizes technical security solutions while neglecting the ethical considerations, protective needs of vulnerable groups, and governance frameworks specific to social work contexts. Research Contribution: This study develops the first systematic multidimensional framework integrating engineering and social science perspectives to evaluate IoT cybersecurity, privacy risks, and governance requirements in social work applications. Using a Systematic Literature Review following PRISMA guidelines, we searched five major databases from January 2020 to September 2024. We employed qualitative thematic analysis combined with an innovative quantitative assessment algorithm to score technologies, threats, and governance components across 55 primary studies. Key Findings: Mental health services and vulnerable population support face “very high” privacy risks (PRS > 8.0), primarily from systemic infrastructure weaknesses in consumer-grade devices rather than sophisticated cyberattacks. Homomorphic encryption achieves the highest security score (9.8/10) but exhibits the highest implementation complexity (9.0/10). Federated learning provides an optimal balance (security 8.5, complexity 8.0, cost 6.0). Ethical guidelines demonstrate the highest implementation difficulty (8.2/10), reflecting challenges in translating abstract principles into technical specifications. Quantitative gap analysis identifies vulnerable population protection as the highest research priority (gap score 3.7/10). This study offers an evidence-driven agenda for practitioners and policymakers, proposing context-specific technology selection criteria and adaptive governance models that prioritize interdisciplinary collaboration, ensuring IoT advancements effectively promote social welfare while protecting at-risk individuals.