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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Crypto-Nets: Neural Networks over Encrypted Data · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1412.6181/</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/1412.6181/feed.xml" rel="self" type="application/rss+xml" /><item><title>Crypto-Nets: Neural Networks over Encrypted Data — mentioned by gwern</title><link>https://news.ycombinator.com/item?id=13011062</link><guid isPermaLink="false">urn:hackerpapers:mention:1412.6181:13011062</guid><pubDate>Tue, 22 Nov 2016 02:10:56 GMT</pubDate><description>&lt;p&gt;You&amp;#x27;re semi-joking, but blockchains could solve this problem cleanly: releasing secret information unless payment is made. In this case, it&amp;#x27;s a subcase of time-lock crypto (http://www.gwern.net/Self-decrypting%20files) and like time-lock crypto in general, can in theory be solved by &amp;#x27;witness encryption&amp;#x27;. Witness crypto lets you create an encrypted file whose decryption key is _X_ Bitcoin blocks (specifically, their SHA hashes which are the PoW); after _X_ blocks have been created after a particular block ID, the file can be decrypted. So that&amp;#x27;s general time-lock crypto, for public broadcast.…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1412.6181/"&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>Crypto-Nets: Neural Networks over Encrypted Data — mentioned by nl</title><link>https://news.ycombinator.com/item?id=8965296</link><guid isPermaLink="false">urn:hackerpapers:mention:1412.6181:8965296</guid><pubDate>Thu, 29 Jan 2015 10:26:53 GMT</pubDate><description>&lt;p&gt;If you are interested in this, the list of papers for the 2015 International Conference on Learning Representations has been released: http://www.iclr.cc/doku.php?id=iclr2015:main#submissions There&amp;#x27;s some pretty good stuff there. I really liked Crypto-Nets: Neural Networks over Encrypted Data[1]: The problem we address is the following: how can a user employ a predictive model that is held by a third party, without compromising private information. For example, a hospital may wish to use a cloud service to predict the readmission risk of a patient. However, due to regulations, the patient&amp;#x27;s…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1412.6181/"&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>Crypto-Nets: Neural Networks over Encrypted Data — HN submission</title><link>https://news.ycombinator.com/item?id=8957388</link><guid isPermaLink="false">urn:hackerpapers:submission:1412.6181:8957388</guid><pubDate>Wed, 28 Jan 2015 01:08:20 GMT</pubDate><description>&lt;p&gt;Crypto-Nets: Neural Networks Over Encrypted Data&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1412.6181/"&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>
