Publication
Machine learning has a lot of potential when applied to time series sensor data, yet a lot of this potential is currently not utilized, due to privacy concerns of parties in charge of this data. In this work I want to apply privacy-preserving techniques to machine learning for time series data, in order to unleash the dormant potential of this type of data.
F. Papst, Privacy-preserving machine learning for time series data: PhD forum abstract, in: SenSys ’20: The 18th ACM Conf on Embedded Networked Sensor Systems (2020) 813–814
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