About Me
Daniel Commey is a cybersecurity researcher working at the intersection of trustworthy AI, distributed systems, applied cryptography, and emerging infrastructure. His research asks how intelligent, distributed, and emerging systems earn, lose, measure, explain, and repair trust in settings where no single participant, model, device, or institution can be assumed fully trusted.
His past work built security mechanisms for blockchain-IoT and federated learning, including hardware-rooted device authentication, adaptive cyber deception, post-quantum collaborative training, and verifiable model evaluation. His current and future work broadens that foundation into trustworthy AI and emerging systems: evidence cards for AI trustworthiness claims, accountability infrastructure for AI agents, contestable AI services, reproducible edge/IoT testbeds, and practical measurement of privacy, security, robustness, fairness, and recourse before deployment.
Research Interests
- Areas: Trustworthy AI, distributed systems, cybersecurity, and privacy-preserving computation for networked, cloud/edge, blockchain-IoT, and cyber-physical environments.
- Methods: Applied cryptography, federated/decentralized learning, verifiable evaluation, measurement-driven systems design, and game-theoretic analysis for cyber defense.
News
- [May 2026] PUFZIN journal article published in Journal of Information Security and Applications Secure and scalable blockchain-IoT authentication with PUFs and zero-knowledge proofs (DOI: 10.1016/j.jisa.2026.104510).
- [May 2026] Completed Ph.D. in Interdisciplinary Engineering at Texas A&M University Dissertation: 'A Layered Security Framework for Blockchain-Based IoT Systems'.
- [May 2026] Two papers accepted for IEEE ICC 2026 and IEEE ICC Workshops 2026 Covering AIS dropout attribution and production Layer-2 network reliability.
- [Feb 2026] PQS-BFL journal article published in Expert Systems with Applications Post-quantum secure blockchain-based federated learning framework (DOI: 10.1016/j.eswa.2026.131449).
- [Jan 2026] FedGraph-VASP preprint posted on arXiv Privacy-preserving federated graph learning with post-quantum security for cross-institution AML.
Selected Publications
PUFZIN: Secure and Scalable Blockchain-IoT with PUFs and Zero-Knowledge Proofs
Daniel Commey, S. G. Hounsinou, G. V. Crosby
Journal of Information Security and Applications, vol. 100, article 104510, 2026
PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework
Daniel Commey, G. V. Crosby
Expert Systems with Applications, 2026
Blockchain-Enabled Dynamic Honeypot Conversion for Resource-Efficient IoT Security
Daniel Commey, M. Nkoom, S. G. Hounsinou, G. V. Crosby
Journal of Information Security and Applications, 2025
Post-Quantum Secure Blockchain-Based Federated Learning Framework for Healthcare Analytics
Daniel Commey, S. G. Hounsinou, G. V. Crosby
IEEE Networking Letters, 2025
Securing Blockchain-Based IoT Systems: A Review
Daniel Commey, B. Mai, S. G. Hounsinou, G. V. Crosby
IEEE Access, vol. 12, pp. 98856--98881, 2024
FedSkipTwin: Digital-Twin-Guided Client Skipping for Communication-Efficient Federated Learning
Daniel Commey, K. Abbad, L. Khoukhi, G. V. Crosby
2026 IEEE 23rd Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA
Securing Blockchain-based IoT Systems with Physical Unclonable Functions and Zero-Knowledge Proofs
Daniel Commey, S. G. Hounsinou, G. V. Crosby
2024 IEEE 49th Conference on Local Computer Networks (LCN), Normandy, France
Research Explainers
- PUFZIN Explained - Device authentication with PUFs + zero-knowledge proofs
- BHICS Explained - Dynamic honeypots that adapt to attackers using ML + game theory
- PQS-BFL Explained - Post-quantum secure federated learning for healthcare
Services & CV
I contribute to the research community through journal reviewing and judging.
- Journal Reviewer: IEEE IoT Journal, IEEE Network, IEEE Trans. Mobile Computing, Elsevier Computer Networks, and more.
- Judge: Texas Science & Engineering Fair; Regional High School Science Bowl.