Lightweight confidentiality framework for TinyML-enabled IoT edge communication

Abstract

Lightweight security mechanisms are essential for Internet of Things (IoT) edge environments, where devices operate under strict constraints in computation, memory, and energy. The emergence of TinyML-enabled edge intelligence introduces new communication security requirements, particularly for protecting compact inference outputs (typically 1-32 bytes, including class labels, confidence scores, or anomaly flags) transmitted over potentially insecure networks. This paper presents a lightweight confidentiality-focused encryption framework based on an enhanced variant of the Tiny Encryption Algorithm (TEA), tailored for TinyML-driven IoT communication. The proposed Enhanced TEA incorporates a plaintext-dependent dynamic key diversification mechanism using SHA-256, improving empirical diffusion and ciphertext randomness while preserving the computational efficiency of ARX-based cipher structures. Beyond algorithmic design, the study develops a system-level secure TinyML-enabled IoT communication architecture, integrating encryption directly into edge inference workflows. The framework is implemented and evaluated on an edge computing platform to assess performance in terms of execution time, memory usage, CPU utilization, communication latency, and energy behavior. Experimental results demonstrate an avalanche effect of 54.69% and ciphertext entropy of 7.66 bits/byte, while maintaining less than 5% throughput degradation and minimal latency overhead in MQTT-based communication. The proposed approach provides a practical lightweight confidentiality mechanism for TinyML-enabled edge computing environments operating under moderate resource constraints. However, as the design focuses on confidentiality, it should be combined with lightweight authentication mechanisms to ensure comprehensive security in real-world deployments.