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• W2805375573 abstract "In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy with respect to the original dataset. We use generative adversarial network to draw privacy-preserving artificial data samples and derive an empirical method to assess the risk of information disclosure in a differential-privacy-like way. Our experiments show that we are able to generate artificial data of high quality and successfully train and validate machine learning models on this data while limiting potential privacy loss." @default.
• W2805375573 created "2018-06-13" @default.
• W2805375573 creator A5041447288 @default.
• W2805375573 creator A5060427576 @default.
• W2805375573 date "2018-03-08" @default.
• W2805375573 modified "2023-10-01" @default.
• W2805375573 title "Generating Artificial Data for Private Deep Learning" @default.
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• W2805375573 hasPublicationYear "2018" @default.
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