Privacy-preserving Contact Context Estimation

接触者追踪应用程序提供有关是否发生了COVID-19严重接触者的数据。这种接触追踪完全基于蓝牙信号。我们计划用声学指示器来扩展这一机制，该指示器可以结合周围环境，即关键接触的环境。记录的数据不会离开设备，但保留隐私的声学场景分类器的持续学习将通过差异私有联邦学习来完成。此外，我们计划在一项关于戴口罩咳嗽症状和语言变化的临床研究中收集额外的声学数据。因此，我们将验证公开可用的音频数据，并为未来的研究扩展公开可用的数据集。

Contact tracing apps provide data about whether a critical COVID-19 contact occurred. This contact tracing is solely based on Bluetoot signals. We plan to extend this mechanism with acustic indicators that incorporate the surrounding, i.e., the context of a critical contact. The recorded data would not leave the device yet privacy-preserving continuous learning of the acustic scene classifiers shall be done via differentially private federated learning. Complementarily, we plan to collect additional acustic data in a clinical study about coughing symtoms and change in speech by wearing a mask. As a result, we would validate publicly available audio data and extend the publicly available datasets for future research.