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

Abstract

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 (