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 › Begutachtung

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	18th ACM Conference on Embedded Networked Sensor Systems: SenSys 2020 - Online, Virtual, Yokohama, Japan Dauer: 16 Nov. 2020 → 19 Nov. 2020 http://sensys.acm.org/2020/ http://sensys.acm.org/2020/index.html

Konferenz

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

ASJC Scopus subject areas

Elektrotechnik und Elektronik
Steuerungs- und Systemtechnik
Computernetzwerke und -kommunikation

Zugriff auf Dokument

https://doi.org/10.1145/3384419.3430566

Andere Dateien und Links

http://www.scopus.com/inward/record.url?scp=85097523757&partnerID=8YFLogxK

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

Dieses zitieren

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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.",

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

Abstract

Konferenz

ASJC Scopus subject areas

Zugriff auf Dokument

Andere Dateien und Links

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Aktivitäten

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

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