The Value of Collaboration in Convex Machine Learning with Differential Privacy

In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differentially-private gradients to minimize the fitness cost of the machine learning model using stochastic gradient descent....

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Vydané v:	Proceedings - IEEE Symposium on Security and Privacy s. 304 - 317
Hlavní autori:	Wu, Nan, Farokhi, Farhad, Smith, David, Kaafar, Mohamed Ali
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.05.2020
Predmet:	Collaboration Computational modeling Data models Data privacy Differential privacy Machine learning Prediction algorithms Privacy Stochastic gradient algorithm Training
ISSN:	2375-1207
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