  Low-frequency audio has been proposed as a promising privacy-preserving
modality to study social dynamics in real-world settings. To this end,
researchers have developed wearable devices that can record audio at
frequencies as low as 1250 Hz to mitigate the automatic extraction of the
verbal content of speech that may contain private details. This paper
investigates the validity of this hypothesis, examining the degree to which
low-frequency speech ensures verbal privacy. It includes simulating a potential
privacy attack in various noise environments. Further, it explores the
trade-off between the performance of voice activity detection, which is
fundamental for understanding social behavior, and privacy-preservation. The
evaluation incorporates subjective human intelligibility and automatic speech
recognition performance, comprehensively analyzing the delicate balance between
effective social behavior analysis and preserving verbal privacy.
