Label Differential Privacy via Aggregation

In many real-world applications, due to recent developments in the privacy landscape, training data may be aggregated to preserve the privacy of sensitive training labels. In the learning from label proportions (LLP) framework, the dataset is partitioned into bags of feature-vectors which are availa...

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Veröffentlicht in:	arXiv.org 2023-11
Hauptverfasser:	Brahmbhatt, Anand, Saket, Rishi, Havaldar, Shreyas, Nasery, Anshul, Aravindan Raghuveer
Format:	Artikel
Sprache:	eng
Schlagworte:	Datasets Labels Learning Lower bounds Privacy Regression Training
Online-Zugang:	Volltext
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