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.
CITATION STYLE
Papst, F. (2020). Privacy-preserving machine learning for time series data: PhD forum abstract. In SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems (pp. 813–814). Association for Computing Machinery, Inc. https://doi.org/10.1145/3384419.3430566
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