Privacy-Preserving Covert Communication Using Encrypted Wearable Gesture Recognition

TL;DR

This paper introduces a novel privacy-preserving gesture recognition system for covert communication using encrypted data, ensuring user privacy and security in sensitive scenarios while maintaining high accuracy and practical deployability.

Contribution

It presents the first encrypted gesture recognition approach for wearable covert communication, preventing data leakage and enabling secure, nonverbal interaction.

Findings

Achieved over 94.44% classification accuracy.

Demonstrated system feasibility on commodity smartwatches.

Ensured no raw or intermediate data is exposed to third parties.

Abstract

Secure communication is essential in covert and safety-critical settings where verbal interactions may expose user intent or operational context. Wearable gesture-based communication enables low-effort, nonverbal interaction, but existing systems leak motion data, intermediate representations, or inference outputs to untrusted infrastructure, enabling intent inference, behavioral biometric leakage, and insider attacks. This work proposes a privacy-preserving gesture-based covert communication system that ensures, no raw sensor signals, learned features, or classification outputs are exposed to any third-party. The system employs a multi-party homomorphic learning pipeline for gesture recognition directly over encrypted motion data, preventing adversaries from inferring gesture semantics, replaying sensor traces, or accessing intermediate representations. To our knowledge, this work is the first to apply encrypted gesture recognition in a wearable-based covert communication setting. We design and evaluate haptic and visual feedback mechanisms for covert signal delivery and evaluate the system using 600 gesture samples from a commodity smartwatch, achieving over 94.44% classification accuracy and demonstrating the feasibility of the proposed system with practical deployability from high-performance systems to resource-constrained edge devices.