  Smartphone motion sensors provide a concealed mechanism for eavesdropping on
acoustic information, like touchtones, emitted by a device. Eavesdropping on
touchtones exposes credit card information, banking pins, and social security
card numbers to malicious 3rd party apps requiring only motion sensor data.
This paper's primary contribution is an analysis rooted in physics and signal
processing theory of several eavesdropping mitigations, which could be
implemented in a smartphone update. We verify our analysis imperially to show
how previously suggested mitigations, i.e. a low-pass filter, can undesirably
reduce the motion sensor data to all applications by 83% but only reduce an
advanced adversary's accuracy by less than one percent. Other designs, i.e.
anti-aliasing filters, can fully preserve the motion sensor data to support
benign application functionality while reducing attack accuracy by 50.1%. We
intend for this analysis to motivate the need for deployable mitigations
against acoustic leakage on smartphone motion sensors, including but not
limited to touchtones, while also providing a basis for future mitigations to
improve upon.
