Characterizing the Effect of Audio Degradation on Privacy Perception And Inference Performance in Audio-Based Human Activity Recognition

Audio has been increasingly adopted as a sensing modality in a variety of human-centered mobile applications and in smart assistants in the home. Although acoustic features can capture complex semantic information about human activities and context, continuous audio recording often poses significant privacy concerns. An intuitive way to reduce privacy concerns is to degrade audio quality such that speech and other relevant acoustic markers become unintelligible, but this often comes at the cost of activity recognition performance. In this paper, we employ a mixed-methods approach to characterize this balance. We first conduct an online survey with 266 participants to capture their perception of privacy qualitatively and quantitatively with degraded audio. Given our findings that privacy concerns can be significantly reduced at high levels of audio degradation, we then investigate how intentional degradation of audio frames can affect the recognition results of the target classes while maintaining effective privacy mitigation. Our results indicate that degradation of audio frames can leave minimal effects for audio recognition using frame-level features. Furthermore, degradation of audio frames can hurt the performance to some extend for audio recognition using segment-level features, though the usage of such features may still yield superior recognition performance. Given the different requirements on privacy mitigation and recognition performance for different sensing purposes, such trade-offs need to be balanced in actual implementations.