With the prevalence of various sensors and smart devices in people’s daily lives, numerous types of information are being sensed. While using such information provides critical and convenient services, we are gradually exposing every piece of our behavior and activities. Researchers are aware of the privacy risks and have been working on preserving privacy while sensing human activities. This survey reviews existing studies on privacy-preserving human activity sensing. We first introduce the sensors and captured private information related to human activities. We then propose a taxonomy to structure the methods for preserving private information from two aspects: individual and collaborative activity sensing. For each of the two aspects, the methods are classified into three levels: signal, algorithm, and system. Finally, we discuss the open challenges and provide future directions.

Device-free acoustic sensing has obsessed with renovating human-computer interaction techniques for all-sized mobile devices in various applications. Recent advances have explored sound signals in different methods to achieve highly accurate and efficient tracking and recognition. However, accuracies of most approaches remain bottlenecked by the limited sampling rate and narrow bandwidth, leading to restrictions and inconvenience in applications. To bridge over the aforementioned daunting barriers, we propose${{\sf PD-FMCW}}$, a novel ultrasound-based device-free tracking scheme that can distinctly improve the resolution of fine-grained sensing to submillimetre level. In its heart lies an original Phase Difference based approach leveraging the Frequency-Modulated Continuous Wave to derive the reflected time delay, thus precisely inferring absolute distance, catering to interaction needs of tinier perception with lower delay. The distance resolution of${{\sf PD-FMCW}}$is only related to the speed of actions and chirp duration. We implement a prototype with effective denoising methods all in the time domain on smartphones. The evaluation results show that${{\sf PD-FMCW}}$achieves accuracies of 2.5 mm, 3.6 mm, and 2.1 mm in distance change, path length change, and trajectory tracking error respectively.${{\sf PD-FMCW}}$is also valid in recognizing 2 mm or even tinier micro-movements, which paves the way for more delicate sensing work.

Device-free acoustic sensing has obsessed with renovating human-computer interaction techniques for all-sized mobile devices in various applications. Recent advances have explored sound signals in different methods to achieve highly accurate and efficient tracking and recognition. However, accuracies of most approaches remain bottlenecked by the limited sampling rate and narrow bandwidth, leading to restrictions and inconvenience in applications. To bridge over the aforementioned daunting barriers, we propose PDF, a novel ultrasound-based device-free tracking scheme that can distinctly improve the resolution of fine-grained sensing to submillimetre level. In its heart lies an original Phase Difference based approach to derive time delay of the reflected Frequency-Modulated Continuous Wave (FMCW), thus precisely inferring absolute distance, catering to interaction needs of tinier perception with lower delay. The distance resolution of PDF is only related to the speed of actions and chirp duration. We implement a prototype with effective denoising methods all in the time domain on smartphones. The evaluation results show that PDF achieves accuracies of 2.5 mm, 3.6 mm, and 2.1 mm in distance change, absolute distance change, and trajectory tracking error respectively. PDF is also valid in recognizing 2 mm or even tinier micro-movements, which paves the way for more delicate sensing work.