Privacy-preserving Contact Context Estimation

• Applicants: Professor Dr. Esfandiar Mohammadi; Professor Dr. Jan Rupp
• Subject Area: Security and Dependability, Operating-, Communication- and Distributed Systems ; Clinical Infectiology and Tropical Medicine
• Term: from 2021 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 492351968

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.

• DFG Programme: Research Grants