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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Overlearning Reveals Sensitive Attributes · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1905.11742/</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/1905.11742/feed.xml" rel="self" type="application/rss+xml" /><item><title>Overlearning Reveals Sensitive Attributes — mentioned by lmkg</title><link>https://news.ycombinator.com/item?id=22250657</link><guid isPermaLink="false">urn:hackerpapers:mention:1905.11742:22250657</guid><pubDate>Wed, 05 Feb 2020 20:27:21 GMT</pubDate><description>&lt;p&gt;This is definitely true. In fact, this can be exploited to extract sensitive/private attributes about the training data from the learned models. This may become an issue for, e.g., AI in healthcare. &amp;quot;Overlearning Reveals Sensitive Attributes&amp;quot;: https://arxiv.org/abs/1905.11742&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1905.11742/"&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>
