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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1911.07101/</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/1911.07101/feed.xml" rel="self" type="application/rss+xml" /><item><title>Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data — mentioned by nindanaoto</title><link>https://news.ycombinator.com/item?id=22866141</link><guid isPermaLink="false">urn:hackerpapers:mention:1911.07101:22866141</guid><pubDate>Tue, 14 Apr 2020 14:08:14 GMT</pubDate><description>&lt;p&gt;TFHE is known to be switchable between other RLWE based Homomorphic Encryption. https://eprint.iacr.org/2018/758 Glyph applied it to accelerate deep learning, but they use integer level, not float. https://arxiv.org/abs/1911.07101 AFAIK, Homomorphic Encryption which supports floating point arithmetic is not known. Fixed point one is known, CKKS. https://github.com/snucrypto/HEAAN&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1911.07101/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
