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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A generic framework for privacy preserving deep learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1811.04017/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1811.04017/feed.xml" rel="self" type="application/rss+xml" /><item><title>A generic framework for privacy preserving deep learning — mentioned by williamtrask</title><link>https://news.ycombinator.com/item?id=18595769</link><guid isPermaLink="false">urn:hackerpapers:mention:1811.04017:18595769</guid><pubDate>Tue, 04 Dec 2018 03:09:52 GMT</pubDate><description>&lt;p&gt;Based on PySyft: https://github.com/OpenMined/PySyft Paper: https://arxiv.org/abs/1811.04017.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1811.04017/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>A generic framework for privacy preserving deep learning — HN submission</title><link>https://news.ycombinator.com/item?id=18431702</link><guid isPermaLink="false">urn:hackerpapers:submission:1811.04017:18431702</guid><pubDate>Mon, 12 Nov 2018 10:28:53 GMT</pubDate><description>&lt;p&gt;Paper: A Generic Framework for Privacy Preserving Deep Learning (NIPS 2018)&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;25 points; 1 discussion reply on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1811.04017/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
