Privacy preservation in sensor-based Human Activity Recognition through autoencoders for low-power IoT devices

Human activity recognition is increasingly recognized as a key task in many applications. However, gathering data from the variety of sensors commonly available on end devices risks compromising user’s privacy when signals are transmitted to more powerful computing units for inference offloading. It is therefore important to design and implement strategies that could prevent privacy breaches without impairing the capability of the system of recognizing activity patterns, and by taking into account the energy constraints of low-power devices. In this work, we propose an energy-aware approach aimed at preserving the privacy of users during inference of human activities. The proposed method is based on a deep learning autoencoder trained to process the signal in order to remove the most sensitive information regarding privacy attributes, without significantly impacting classification accuracy. We also perform a thorough architecture’s parameter tuning of the designed system to enable its implementation on a low- power platform, which we also characterize in terms of energy expenditure. Experimental results show that this system is capable of effectively transforming the signal in order to prevent the inference of sensitive attributes (i.e. weight, height, age, and gender) and it can be conveniently implemented on a constrained embedded system at different levels of the trade-off between accuracy and energy consumption. Indeed, a complete obfuscation of sensitive attributes can be achieved at the cost of a marginal reduction in classification accuracy (5% at most), with an expenditure of around 165 mJ for an execution time of around 30ms needed during the signal transformation step.