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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2506.18150/</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/2506.18150/feed.xml" rel="self" type="application/rss+xml" /><item><title>HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption — mentioned by jerf</title><link>https://news.ycombinator.com/item?id=49302013</link><guid isPermaLink="false">urn:hackerpapers:mention:2506.18150:49302013</guid><pubDate>Fri, 14 Aug 2026 17:37:39 GMT</pubDate><description>&lt;p&gt;The article conspicuously fails to go into much detail about that. I poked around with an AI a bit (to rapidly cover all the linked pages) and it seems the best numbers we can get are from this arxiv paper: https://arxiv.org/html/2506.18150v4 Which says: &amp;quot;We evaluate HE-LRM on UCI (health prediction) and Criteo (click prediction), achieving inference latencies of 24 seconds on UCI and 228 to 489 seconds, respectively, on a single-threaded CPU.&amp;quot; There don&amp;#x27;t seem to be any direct comparisons available, probably because nobody else has any reason to limit themselves to one single-threaded CPU…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2506.18150/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CR</category></item></channel></rss>
