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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sensitive Data Detection with High-Throughput Neural Network Models for Financial Institutions · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2012.09597/</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/2012.09597/feed.xml" rel="self" type="application/rss+xml" /><item><title>Sensitive Data Detection with High-Throughput Neural Network Models for Financial Institutions — mentioned by lettergram</title><link>https://news.ycombinator.com/item?id=26599662</link><guid isPermaLink="false">urn:hackerpapers:mention:2012.09597:26599662</guid><pubDate>Sat, 27 Mar 2021 03:26:44 GMT</pubDate><description>&lt;p&gt;It was actually a response to your comment: &amp;gt; One thing I didn&amp;#x27;t get is this magical PII thing. How does the author look at a random network packet -- nay, just packet headers -- and assign a PII:true/false label? I think many corporations would sacrifice the right hand of a sysadmin if that was the way to get this tech. Checkout Amazon macie or Microsoft presidio or try actually using the library I linked? It’s usually used in a constrained way, in no way perfect. But it helps investigators track suspected cases of data exfiltration. You can pull something that looks suspect (say a credit…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2012.09597/"&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>
