Stochastic Modeling of Human-Machine Authentication Channels under Partial Information Leakage

2026-05-03

AI Summary

Traditional security evaluations often treat PIN authentication as either fully secure or completely compromised, overlooking the gradual degradation of reliability caused by partial information leakage in IoT scenarios. This work models PIN entry as a stochastic human–computer communication system and introduces a context-aware conditional probability inference framework to quantify security loss under partial symbol exposure. By employing smoothed conditional distributions and fallback prior estimation to infer missing digits, the approach eschews explicit modeling of sequential positional dependencies in favor of context-driven probabilistic inference that captures latent cross-position correlations more aligned with real user behavior. Experiments on a million-scale real-world PIN dataset demonstrate prediction accuracies of 55.31% and 12.12% under single-, double-, and triple-digit leakage scenarios, substantially outperforming standard sequential models and classical machine learning methods.

Abstract

Reliable and secure human-machine communication is fundamental to IoT and cyber-physical ecosystems, where smartphones and wearables commonly serve as authentication controllers. PIN-based authentication can be viewed as a low-bandwidth communication channel through which users transmit numeric credentials under practical constraints. However, conventional evaluations adopt a binary view of security-treating such channels as either fully secure or fully compromised-thereby overlooking the progressive reliability degradation caused by partial information leakage in real-world IoT settings. In this paper, we model the PIN entry process as a stochastic human-IoT communication system and propose a context-conditioned probabilistic inference framework to quantify reliability loss and Quality-of-Service degradation under partial symbol exposure. The proposed approach treats missing digits as latent variables and estimates them using smoothed conditional probability distributions with fallback priors. Unlike traditional sequential models that assume contiguous positional dependencies, the method does not explicitly parameterize hidden-state transitions or emissions; instead, it performs context-driven probabilistic inference to approximate latent dependencies across digit positions. Using over one million real-world four-digit PIN samples, we evaluate single-, double-, and triple-digit leakage scenarios and derive position-dependent reliability metrics. The proposed model achieves up to 55.31% prediction accuracy for one missing digit and 12.12% for three missing digits, while consistently outperforming a standard sequence-model baseline and classical machine learning models in terms of precision, recall, and F1-score. These results formalize PIN entry as a noisy human--IoT communication channel and demonstrate substantial reliability degradation under realistic partial exposure conditions.

Problem

• human-machine authentication
• partial information leakage
• PIN-based authentication
• reliability degradation
• IoT security

Innovation

• stochastic modeling
• partial information leakage
• context-conditioned inference
• human-machine authentication
• latent variable estimation