Privacy-Preserving Covert Communication Using Encrypted Wearable Gesture Recognition

2026-02-08

AI Summary

This work proposes the first end-to-end privacy-preserving system for wearable covert gesture communication, addressing the vulnerability of existing approaches that risk exposing raw sensor data, intermediate features, or classification outputs in untrusted environments—potentially enabling inference of user intent or leakage of biometric information. By leveraging multi-party homomorphic encryption, the system performs gesture recognition directly on encrypted motion data from commercial smartwatches and other edge devices, ensuring that neither the original signals, extracted features, nor the final predictions are ever exposed in plaintext. Integrated tactile and visual feedback mechanisms enable seamless user interaction, and the system achieves 94.44% accuracy on a dataset of 600 gestures while effectively mitigating semantic inference, trajectory replay, and intermediate representation leakage, demonstrating its practicality and deployability in resource-constrained settings.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Biometrics, Face, Gesture & Pose
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• privacy-preserving
• covert communication
• gesture recognition
• wearable security
• data leakage

Innovation

• privacy-preserving
• homomorphic learning
• gesture recognition
• covert communication
• wearable computing