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

Franz Papst*

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: KonferenzbeitragAbstract

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.
Originalspracheenglisch
Seiten813-814
Seitenumfang2
DOIs
PublikationsstatusVeröffentlicht - 16 Nov 2020
VeranstaltungThe 18th ACM Conference on Embedded Networked Sensor Systems - Virtuell, Japan
Dauer: 16 Nov 202019 Nov 2020
http://sensys.acm.org/2020/index.html

Konferenz

KonferenzThe 18th ACM Conference on Embedded Networked Sensor Systems
KurztitelSenSys ’20
LandJapan
OrtVirtuell
Zeitraum16/11/2019/11/20
Internetadresse

ASJC Scopus subject areas

  • !!Electrical and Electronic Engineering
  • !!Control and Systems Engineering
  • !!Computer Networks and Communications

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