Neutrosophic Logic for Secure Hand-Based Biometrics: Quantifying Privacy-Security Tradeoffs in Remote Authentication Systems

Remote biometric authentication systems, particularly those relying on hand-based modalities (e.g., fingerprints, palm prints, and hand geometry), encounter critical security and privacy challenges in networked environments, and this conventional analytical approaches often struggle to account for the inherent uncertainties in these systems. To address this gap, we propose a vulnerability assessment framework grounded in neutrosophic logic [11], which evaluates system robustness through truth (T), indeterminacy (I), and falsity (F) membership functions, and this approach quantifies the trade-offs between security and privacy, revealing that hand-based biometrics achieve 92% security effectiveness (T = 0.8) while retaining an 18% uncertainty factor ( 𝐼 = 0.4 ) concerning potential vulnerabilities, and added analysis further identifies deficiencies in template protection (F = 0.2) and data transmission protocols, and we, proposed framework advances us the evaluation of biometric systems by using integrating neutrosophic uncertainty modeling, and it provides actionable insights for designing secure remote authentication architectures. These results emphasize us the necessity of multi-layered security strategies that harmonize high-confidence authentication with stringent privacy preserve.