Browser fingerprinting: how to protect machine learning models and data with differential privacy?

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Notes: In: 1st International Workshop on Machine Learning in Networking (MaLeNe), part of the Conference on Networked Systems 2021 (NetSys 2021), September 13-16, 2021, Lübeck, Germany, S.

Bibliographic citation: Electronic Communications of the EASST ; 80

Event: Veröffentlichung

(where): Augsburg

(who): Universität Augsburg

(when): 2021

Event: Veröffentlichung

(where): Berlin

(who): Universitätsbibliothek TU Berlin

(when): 2021

Creator: Dietz, Katharina
Mühlhauser, Michael
Seufert, Michael Thomas
Gray, Nicholas
Hoßfeld, Tobias
Herrmann, Dominik

Contributor: Fischer, Mathias
Lamerdorf, Winfried

DOI: 10.14279/tuj.eceasst.80.1179

URN: urn:nbn:de:bvb:384-opus4-1073461

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 25.03.2025, 1:53 PM CET

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Associated

Dietz, Katharina
Mühlhauser, Michael
Seufert, Michael Thomas
Gray, Nicholas
Hoßfeld, Tobias
Herrmann, Dominik
Fischer, Mathias
Lamerdorf, Winfried
Universität Augsburg
Universitätsbibliothek TU Berlin

Time of origin

2021

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Browser fingerprinting: how to protect machine learning models and data with differential privacy?

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Other Objects (12)

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