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.

翻译：智能手机感应器为窃听声学信息提供了一个隐蔽机制。 窃听触摸器的装置所释放的触摸器等声学信息提供了一个隐蔽机制。 窃听触摸器将信用卡信息、 银行针和社会保障卡号码暴露在恶意的三方应用程序中, 只要求运动感应器数据。 本文的主要贡献是基于物理和信号处理理论的数种窃听器减缓信号分析, 可以在智能手机更新中实施。 我们通过帝国主义分析来显示我们先前建议的缓解措施, 即低通道过滤器, 如何将运动感应数据不可取地减少83%, 但只会将高级对手的准确性降低不到1%。 其他设计, 即反丑化过滤器, 可以充分保存运动感应数据以支持良性应用功能, 同时将攻击精确度降低50.1%。 我们打算进行这项分析, 以激励需要针对智能手机感应感应感应感应感应感应感应的调漏, 包括但不局限于触控器, 同时也为未来减缓措施的改善的基础。