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

Franz Papst

doi:https://doi.org/10.1145/3384419.3430566

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

Franz Papst^*

^*Korrespondierende/r Autor/in für diese Arbeit

Institut für Technische Informatik (4480)

Publikation: Konferenzbeitrag › 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.

Originalsprache	englisch
Seiten	813-814
Seitenumfang	2
DOIs	https://doi.org/10.1145/3384419.3430566
Publikationsstatus	Veröffentlicht - 16 Nov 2020
Veranstaltung	The 18th ACM Conference on Embedded Networked Sensor Systems - Virtuell, Japan Dauer: 16 Nov 2020 → 19 Nov 2020 http://sensys.acm.org/2020/index.html

Konferenz

Konferenz	The 18th ACM Conference on Embedded Networked Sensor Systems
Kurztitel	SenSys ’20
Land	Japan
Ort	Virtuell
Zeitraum	16/11/20 → 19/11/20
Internetadresse	http://sensys.acm.org/2020/index.html

ASJC Scopus subject areas

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

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https://doi.org/10.1145/3384419.3430566

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1 Vortrag bei Workshop, Seminar oder Kurs

PhD Forum Abstract: Privacy-Preserving Machine Learning for Time Series Data
Franz Papst (Redner/in)
14 Nov 2020
Aktivität: Vortrag oder Präsentation › Vortrag bei Workshop, Seminar oder Kurs › Science to science

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

Abstract

Konferenz

ASJC Scopus subject areas

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PhD Forum Abstract: Privacy-Preserving Machine Learning for Time Series Data

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