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

Franz Papst*

*Corresponding author for this work

Research output: Contribution to conference › Abstract

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 languageEnglish
Pages813-814
Number of pages2
DOIs
Publication statusPublished - 16 Nov 2020
EventThe 18th ACM Conference on Embedded Networked Sensor Systems - Virtuell, Japan
Duration: 16 Nov 2020 → 19 Nov 2020
http://sensys.acm.org/2020/index.html

Conference

ConferenceThe 18th ACM Conference on Embedded Networked Sensor Systems
Abbreviated titleSenSys ’20
CountryJapan
CityVirtuell
Period16/11/20 → 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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