Description
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.Period | 14 Nov 2020 |
---|---|
Event title | 18th ACM Conference on Embedded Networked Sensor Systems: SenSys 2020 |
Event type | Conference |
Location | Virtual, Yokohama, JapanShow on map |
Degree of Recognition | International |
Related content
-
Publications
-
Privacy-Preserving Machine Learning for Time Series Data: PhD forum abstract
Research output: Contribution to conference › Abstract › peer-review