Patronus: Preventing Unauthorized Speech Recordings with Support for Selective Unscrambling

Abstract

The widespread adoption and ubiquity of smart devices equipped with microphones (e.g., cellphones, smartwatches, etc.) unfortunately create many significant privacy risks. In recent years, there have been several cases of people’s conversations being secretly recorded, sometimes initiated by the device itself. Although some manufacturers are trying to protect users’ privacy, to the best of our knowledge, there is not any effective technical solution available. In this work, we present Patronus, a system that can both prevent unauthorized devices from making secret recordings while allowing authorized devices to record conversations. Patronus prevents unauthorized speech recording by emitting what we call a scramble, a low-frequency noise generated by inaudible ultrasonic waves. The scramble prevents unauthorized recordings by leveraging the nonlinear effects of commercial off-the-shelf microphones. The frequency components of the scramble are randomly determined and connected with linear chirps, and the frequency period is fine-tuned so that the scramble pattern is hard to attack. Patronus allows authorized speech recording by secretly delivering the scramble pattern to authorized devices, which can use an adaptive filter to cancel out the scramble. We implement a prototype system and conduct comprehensive experiments. Our results show that only 19.7% of words protected by Patronus’ scramble can be recognized by unauthorized devices. Furthermore, authorized recordings have 1.6x higher perceptual evaluation of speech quality (PESQ) score and, on average, 50% lower speech recognition error rates than unauthorized recordings.

Fan DANG

Research Assistant Professor

My research interests include industrial intelligence and edge computing.