Adversarial representation learning for synthetic replacement of private attributes

Data privacy is an increasingly important aspect of many real-world Data sources that contain sensitive information may have immense potential which could be unlocked using the right privacy enhancing transformations, but current methods often fail to produce convincing output. Furthermore, finding...

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Veröffentlicht in:	arXiv.org 2021-02
Hauptverfasser:	Martinsson, John, Zec, Edvin Listo, Gillblad, Daniel, Mogren, Olof
Format:	Artikel
Sprache:	eng
Schlagworte:	Cognition Data collection Learning Privacy Privatization Representations
Online-Zugang:	Volltext
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