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Stochastic Modeling of Human-Machine Authentication Channels under Partial Information Leakage
May 3, 2026Nilesh Chakraborty, Mohammad Zulkernine, Burak KantarciAuthentication Data Leakage
University of Ottawa, Ottawa, ON, Canada · School of Computing, Queen's University, Kingston, ON, Canada
Data driven approach for Outdoor Channel Prediction in 5G and Beyond
May 3, 2026A. Sathi Babu, V. Udaya Sankar, Vishnu Ram OVChannel Estimation Channel Modeling
Department of ECE SRM University AP Guntur, India · Independent Research Consultant Bengaluru, India
Null-Space Flow Matching for MIMO Channel Estimation in Latency-Constrained Systems
Apr 23, 2026Junjie Zhao, Guangming Liang, Dongzhu Liu +1Channel Estimation Channel State Information
College of Electronic Information and Optical Engineering, Nankai University · School of Computing Science, University of Glasgow · School of Natural and Computing Science, University of Aberdeen
Channel-Free Human Activity Recognition via Inductive-Bias-Aware Fusion Design for Heterogeneous IoT Sensor Environments
Apr 23, 2026Tatsuhito HasegawaHuman Activity Recognition Global-Local Fusion Framework
Graduate School of Engineering, University of Fukui, Bunkyo, Fukui, 9108507, Fukui, Japan
Polysemantic Experts, Monosemantic Paths: Routing as Control in MoEs
Apr 20, 2026Charles Ye, Bo Yuan, Lee SharkeyMixture-Of-Experts Layer-Wise
Independent · Georgia Institute of Technology · Goodfire
Channel Attention-Guided Cross-Modal Knowledge Distillation for Referring Image Segmentation
Apr 18, 2026Chen YangCross-Modal Distillation Channel
School of Information Science and Technology, TaiShan University, Taian, China
The Query Channel: Information-Theoretic Limits of Masking-Based Explanations
Apr 17, 2026Erciyes Karakaya, Ozgur ErcetinInformation-Theoretic Limits Explainable AI Methods
*Department of Electrical and Computer Engineering, University of Maryland, College Park, USA · †Faculty of Engineering and Natural Sciences, Sabanci University, Turkiye
AI-Enabled Covert Channel Detection in RF Receiver Architectures
Apr 16, 2026Abdelrahman Emad Abdelazim, Alan Rodrigo Diaz-Rizo, Hassan Aboushady +1Channel State Information Channel
Sorbonne Universit´e, CNRS, LIP6, Paris, France
Information Thermodynamics of Agents: The Work Capacity of Channels with Memory
Apr 8, 2025Lukas J. Fiderer, Paul C. Barth, Isaac D. Smith +1Thermodynamics Free Energy Principle
Universität Innsbruck, Institut für Theoretische Physik, Technikerstraße 21a, A-6020 Innsbruck, Austria
Semidefinite Programming for Quantum Channel Learning
Date pendingMikhail Gennadievich Belov, Victor Victorovich Dubov, Vadim Konstantinovich Ivanov +3Convex Relaxation Channel
Lomonosov Moscow State University, Faculty of Mechanics and Mathematics, GSP-1, Moscow, Vorob’evy Gory, 119991, Russia and Autretech Group, Skolkovo Innovation Center, Nobel Street, Building 7, Moscow, 121205, Russia · Peter the Great St. Petersburg Polytechnic University, 195251, Russia · Ioffe Institute, Politekhnicheskaya 26, St Petersburg, 194021, Russia

May 3, 2026, cs.CRJ/K move · Enter open · S save

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

Nilesh Chakraborty, Mohammad Zulkernine, Burak Kantarci

University of Ottawa, Ottawa, ON, Canada · School of Computing, Queen's University, Kingston, ON, Canada

Authentication Data Leakage Probabilistic Inference Channel

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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.

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