Physically Unclonable Functions for Secure IoT Authentication and Hardware-Anchored AI Model Integrity

2026-04-22

AI Summary

This study addresses the critical need for hardware-rooted trust mechanisms in resource-constrained and physically exposed AIoT systems that effectively balance security and practicality. It presents the first systematic survey and comparative analysis of Trusted Platform Modules (TPMs), silicon- and FPGA-based Physically Unclonable Functions (PUFs), container-aware hybrid roots of trust, and purely software-based approaches, evaluating their trade-offs across key dimensions including security, scalability, cost, and deployment complexity in AIoT contexts. The findings demonstrate that PUFs and hybrid architectures offer significant advantages in resisting physical attacks and device cloning, thereby providing essential design guidance for building trustworthy edge AI platforms.

Technology Category

• Machine Learning: Hardware-aware ML
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The rapid integration of artificial intelligence (AI) into Internet of Things (IoT) and edge computing systems has intensified the need for robust, hardware-rooted trust mechanisms capable of ensuring device authenticity and AI model integrity under strict resource and security constraints. This survey reviews and synthesizes existing literature on hardware-rooted trust mechanisms for AI-enabled IoT systems. It systematically examines and compares representative trust anchor mechanisms, including Trusted Platform Module (TPM)-based measurement and attestation, silicon and FPGA-based Physical Unclonable Functions (PUFs), hybrid container-aware hardware roots of trust, and software-only security approaches. The analysis highlights how hardware-rooted solutions generally provide stronger protection against physical tampering and device cloning compared to software-only approaches, particularly in adversarial and physically exposed environments, while hybrid designs extend hardware trust into runtime and containerized environments commonly used in modern edge deployments. By evaluating trade-offs among security strength, scalability, cost, and deployment complexity, the study shows that PUF-based and hybrid trust anchors offer a promising balance for large-scale, AI-enabled IoT systems, whereas software-only trust mechanisms remain insufficient in adversarial and physically exposed settings. The presented comparison aims to clarify current design challenges and guide future development of trustworthy AI-enabled IoT platforms.

Problem

• Physically Unclonable Functions
• IoT Authentication
• AI Model Integrity
• Hardware Root of Trust
• Edge Computing Security

Innovation

• Physical Unclonable Functions
• Hardware Root of Trust
• AI Model Integrity
• IoT Security
• Hybrid Trust Anchors