Privacy-Preserving Machine Learning for Time Series Data: PhD forum abstract

Franz Papst*

*Corresponding author for this work

Research output: Contribution to conferenceAbstractpeer-review


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 languageEnglish
Number of pages2
Publication statusPublished - 16 Nov 2020
Event18th ACM Conference on Embedded Networked Sensor Systems: SenSys 2020 - Online, Virtual, Yokohama, Japan
Duration: 16 Nov 202019 Nov 2020


Conference18th ACM Conference on Embedded Networked Sensor Systems
Abbreviated titleSenSys
CityVirtual, Yokohama
Internet address


  • 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


Dive into the research topics of 'Privacy-Preserving Machine Learning for Time Series Data: PhD forum abstract'. Together they form a unique fingerprint.

Cite this