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Abstract
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.
Original language | English |
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Pages | 813-814 |
Number of pages | 2 |
DOIs | |
Publication status | Published - 16 Nov 2020 |
Event | 18th ACM Conference on Embedded Networked Sensor Systems: SenSys 2020 - Online, Virtual, Yokohama, Japan Duration: 16 Nov 2020 → 19 Nov 2020 http://sensys.acm.org/2020/ http://sensys.acm.org/2020/index.html |
Conference
Conference | 18th ACM Conference on Embedded Networked Sensor Systems |
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Abbreviated title | SenSys |
Country/Territory | Japan |
City | Virtual, Yokohama |
Period | 16/11/20 → 19/11/20 |
Internet address |
Keywords
- privacy preserving machine learning
- sensor data
- time series data
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Computer Networks and Communications
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PhD Forum Abstract: Privacy-Preserving Machine Learning for Time Series Data
Franz Papst (Speaker)
14 Nov 2020Activity: Talk or presentation › Talk at workshop, seminar or course › Science to science