Activity Recognition Protection for IoT Trigger-Action Platforms
Mahmoud Aghvamipanah, Morteza Amini, Cyrille Valentin Artho, Musard Balliu · 2024
Smart home devices collect and transmit user data to smart home Trigger Action Platforms (TAPs) for processing and executing automation rules. However, this data can also be used to infer user activities or other sensitive information. In this paper, we propose PTAP, a privacy-preserving approach based on adversarial example attacks. PTAP injects targeted perturbations into time-series sensor data, effectively confounding potentially malicious TAP classifiers. Our approach significantly reduces the chance of user activity recognition for a malicious TAP while preserving the essential information for automation rule execution, thus safeguarding TAP utility. We evaluated PTAP using a real-world smart-home dataset and examined its effectiveness in preserving utility through the execution of various IoT applications. Our results demonstrate that PTAP effectively preserves user privacy (reducing the accuracy of a malicious classifier 91 to 6 percent) while maintaining automation rule integrity, providing a practical and effective solution to protect user privacy in smart-home environments.