<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hacker News: furkanturan</title><link>https://news.ycombinator.com/user?id=furkanturan</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Wed, 19 Aug 2026 12:59:43 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=furkanturan" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by furkanturan in "Google is making private AI practical with homomorphic encryption"]]></title><description><![CDATA[
<p>Regarding the SWIFT example, transactions between banks across countries go through SWIFT’s infrastructure and are encrypted for regulatory and security reasons. Banks and financial institutions operating at this scale are also required to use HSMs (Hardware Security Modules) to meet security and compliance standards, ensuring that sensitive data is processed securely.<p>Suggesting alternative approaches to such large corporations is not straightforward. Their IT and security teams cannot afford to risk handling sensitive financial data in plaintext. No manager would readily approve a solution that offers additional functionality at the cost of deviating, even slightly, from well-established compliance practices. Encryption is therefore fundamental to how these systems operate.<p>The challenge arises when these institutions need to perform computations or checks on such encrypted data. Decrypting the data, even under controlled conditions and with trained personnel, is operationally complex, introduces additional risk, and can create a significant compliance burden.<p>This is where FHE offers a compelling solution. We demonstrate how a check can be performed directly on encrypted transactions against a known blacklist, without requiring the underlying transaction data to be decrypted.<p>P.S. It is great to work with Marc :)</p>
]]></description><pubDate>Sat, 15 Aug 2026 18:09:35 +0000</pubDate><link>https://news.ycombinator.com/item?id=49312822</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=49312822</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49312822</guid></item><item><title><![CDATA[New comment by furkanturan in "Splitting a Git Commit"]]></title><description><![CDATA[
<p>I never thought about whether this was possible, but now that I know it is, I immediately see how useful it could be for my workflow.</p>
]]></description><pubDate>Sat, 15 Aug 2026 14:10:21 +0000</pubDate><link>https://news.ycombinator.com/item?id=49310738</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=49310738</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49310738</guid></item><item><title><![CDATA[New comment by furkanturan in "Google is making private AI practical with homomorphic encryption"]]></title><description><![CDATA[
<p>That is the key advantage of FHE: you encrypt your data locally, using keys that you own, and never need to share the plaintext—or the keys—with anyone.<p>But there is more to it. Teams like us at Belfort, we are working to make FHE practical for organizations that want to deploy it for their own use cases, including on infrastructure they control themselves. Even in these setups, FHE can enable applications that would otherwise be blocked by regulatory, privacy, or compliance requirements.</p>
]]></description><pubDate>Sat, 15 Aug 2026 10:20:02 +0000</pubDate><link>https://news.ycombinator.com/item?id=49309368</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=49309368</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49309368</guid></item><item><title><![CDATA[New comment by furkanturan in "Google is making private AI practical with homomorphic encryption"]]></title><description><![CDATA[
<p>While encrypted AI chatbots seem within reach, we don’t see them as the primary target today. There is a set of untapped applications, such as inter-bank transfers, industrial use cases, and healthcare; where regulations, privacy laws, and compliance requirements prevent institutions from directly accessing or processing sensitive data.<p>This is where we believe Belfort can make a real difference: enabling organizations to compute on sensitive data while preserving strong privacy guarantees. And FHE doesn’t necessarily mean relying on Google, AWS, or Azure for encrypted compute. Companies can run FHE on their own infrastructure, allowing them to process their clients’ data privately while maintaining control over their systems and keys.<p>These applications are our initial focus, and over time, we aim to expand the range of use cases that FHE can unlock.</p>
]]></description><pubDate>Sat, 15 Aug 2026 10:07:34 +0000</pubDate><link>https://news.ycombinator.com/item?id=49309303</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=49309303</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49309303</guid></item><item><title><![CDATA[New comment by furkanturan in "Google is making private AI practical with homomorphic encryption"]]></title><description><![CDATA[
<p>Thanks a lot. We have some road ahead for using encrypted AI for your agentic work. Today, I would not suggest thinking that as the primary target. Instead, there are many untapped applications (e.g. inter-bank transfers, industry, healthcare) where regulations, privacy laws and compliance requirements restrict institutions from touching data. These will initially be our key enablers, and over time we hope to extend the range of applications. At Belfort we are exploring such use cases that will benefit our FHE acceleration.</p>
]]></description><pubDate>Sat, 15 Aug 2026 10:01:27 +0000</pubDate><link>https://news.ycombinator.com/item?id=49309268</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=49309268</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49309268</guid></item><item><title><![CDATA[New comment by furkanturan in "Google is making private AI practical with homomorphic encryption"]]></title><description><![CDATA[
<p>FHE is not always for using somebody else's server; but for sure you do not share keys with that somebody else.<p>Besides, FHE is not always about using somebody else's server. At Belfort, in addition to FHE acceleration, we also explore such uses cases; <a href="https://belfortlabs.com/blog/encrypted-fraud-detection-with-swift" rel="nofollow">https://belfortlabs.com/blog/encrypted-fraud-detection-with-...</a> <a href="https://belfortlabs.com/blog/belfort-partners-with-lg-on-encrypted-advertising-recommendations" rel="nofollow">https://belfortlabs.com/blog/belfort-partners-with-lg-on-enc...</a></p>
]]></description><pubDate>Sat, 15 Aug 2026 09:56:22 +0000</pubDate><link>https://news.ycombinator.com/item?id=49309237</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=49309237</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49309237</guid></item><item><title><![CDATA[New comment by furkanturan in "Google is making private AI practical with homomorphic encryption"]]></title><description><![CDATA[
<p>There are many challenges we need to solve for privacy preserving compute. Legislations, legal matters, key management, HSM like devices that has limited compute capabilities. At Belfort, we are accelerating FHE, besides exploring such uses cases for FHE. Like this one with Google, we also have publishes use cases; <a href="https://belfortlabs.com/blog/encrypted-fraud-detection-with-swift" rel="nofollow">https://belfortlabs.com/blog/encrypted-fraud-detection-with-...</a> <a href="https://belfortlabs.com/blog/belfort-partners-with-lg-on-encrypted-advertising-recommendations" rel="nofollow">https://belfortlabs.com/blog/belfort-partners-with-lg-on-enc...</a></p>
]]></description><pubDate>Sat, 15 Aug 2026 09:53:21 +0000</pubDate><link>https://news.ycombinator.com/item?id=49309215</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=49309215</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49309215</guid></item><item><title><![CDATA[New comment by furkanturan in "Google is making private AI practical with homomorphic encryption"]]></title><description><![CDATA[
<p>FHE doesn’t need Google too. Google research is investing in to it. Like we are at Belfort, various teams work on making FHE practical for organizations that want to run it for their own use cases, including on infrastructure they control themselves. Even in these setups, FHE can enable applications that would otherwise be blocked by regulatory or privacy requirements. Check out; <a href="https://belfortlabs.com/blog/belfort-partners-with-lg-on-encrypted-advertising-recommendations" rel="nofollow">https://belfortlabs.com/blog/belfort-partners-with-lg-on-enc...</a> <a href="https://belfortlabs.com/blog/encrypted-fraud-detection-with-swift" rel="nofollow">https://belfortlabs.com/blog/encrypted-fraud-detection-with-...</a></p>
]]></description><pubDate>Sat, 15 Aug 2026 09:43:37 +0000</pubDate><link>https://news.ycombinator.com/item?id=49309163</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=49309163</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49309163</guid></item><item><title><![CDATA[New comment by furkanturan in "Google is making private AI practical with homomorphic encryption"]]></title><description><![CDATA[
<p>Yes, it is. But it’s important to recognize that there are different FHE schemes. Zama has invested heavily in TFHE and achieved impressive results. However, time has shown that TFHE is not the best fit for encrypted AI workloads, with CKKS and related schemes gaining an advantage with mathematical and algorithmic advances continue. At Belfort we have foot on both sides; FPGA acceleration for integrating to Zama's TFHE-rs, and GPU acceleration for CKKS.</p>
]]></description><pubDate>Sat, 15 Aug 2026 09:37:56 +0000</pubDate><link>https://news.ycombinator.com/item?id=49309128</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=49309128</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49309128</guid></item><item><title><![CDATA[New comment by furkanturan in "Google is making private AI practical with homomorphic encryption"]]></title><description><![CDATA[
<p>Count Belfort too. In addition to our GPU acceleration efforts, we have ongoing ASIC initiatives to further accelerate encrypted compute.</p>
]]></description><pubDate>Sat, 15 Aug 2026 09:22:19 +0000</pubDate><link>https://news.ycombinator.com/item?id=49309059</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=49309059</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49309059</guid></item><item><title><![CDATA[New comment by furkanturan in "Google is making private AI practical with homomorphic encryption"]]></title><description><![CDATA[
<p>Local compute is preferable where possible. There are cases where computation needs to be performed remotely. For example, when collecting data from remote entities while preserving privacy by allowing each entity to retain ownership of the encryption keys used to protect its data. At Belfort, we are exploring such applications, such as<p>- <a href="https://belfortlabs.com/blog/belfort-partners-with-lg-on-encrypted-advertising-recommendations" rel="nofollow">https://belfortlabs.com/blog/belfort-partners-with-lg-on-enc...</a>
- <a href="https://belfortlabs.com/blog/encrypted-fraud-detection-with-swift" rel="nofollow">https://belfortlabs.com/blog/encrypted-fraud-detection-with-...</a></p>
]]></description><pubDate>Sat, 15 Aug 2026 09:20:04 +0000</pubDate><link>https://news.ycombinator.com/item?id=49309048</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=49309048</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49309048</guid></item><item><title><![CDATA[New comment by furkanturan in "Google is making private AI practical with homomorphic encryption"]]></title><description><![CDATA[
<p>Exactly. That is what we have today at Belfort; not enough for making all AI work privacy preserving, but fast enough for many applications, where otherwise unencrypted compute is not acceptable.</p>
]]></description><pubDate>Sat, 15 Aug 2026 09:14:33 +0000</pubDate><link>https://news.ycombinator.com/item?id=49309026</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=49309026</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49309026</guid></item><item><title><![CDATA[Recommendation engine knows what to show you, without knowing anything about you]]></title><description><![CDATA[
<p>Article URL: <a href="https://belfortlabs.com/blog/lg">https://belfortlabs.com/blog/lg</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=49197380">https://news.ycombinator.com/item?id=49197380</a></p>
<p>Points: 9</p>
<p># Comments: 0</p>
]]></description><pubDate>Thu, 06 Aug 2026 14:42:02 +0000</pubDate><link>https://belfortlabs.com/blog/lg</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=49197380</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49197380</guid></item><item><title><![CDATA[New comment by furkanturan in "Homomorphically encrypted CIFAR-10 inference in 200ms"]]></title><description><![CDATA[
<p>Check this one too: <a href="https://openmined.org/blog/ckks-explained-part-1-simple-encoding-and-decoding/" rel="nofollow">https://openmined.org/blog/ckks-explained-part-1-simple-enco...</a></p>
]]></description><pubDate>Fri, 17 Jul 2026 20:52:04 +0000</pubDate><link>https://news.ycombinator.com/item?id=48952179</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=48952179</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48952179</guid></item><item><title><![CDATA[New comment by furkanturan in "Homomorphically encrypted CIFAR-10 inference in 200ms"]]></title><description><![CDATA[
<p>Good suggestion. Thanks a lot. Definitely will keep in mind for the next demo</p>
]]></description><pubDate>Fri, 17 Jul 2026 20:44:00 +0000</pubDate><link>https://news.ycombinator.com/item?id=48952091</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=48952091</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48952091</guid></item><item><title><![CDATA[New comment by furkanturan in "Homomorphically encrypted CIFAR-10 inference in 200ms"]]></title><description><![CDATA[
<p>Nothing is removed. I am not good with dog breeds, but with "German Shepherd", they probably meant the image #27; the black dog. It is indeed classified as a cat :(</p>
]]></description><pubDate>Fri, 17 Jul 2026 20:20:10 +0000</pubDate><link>https://news.ycombinator.com/item?id=48951856</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=48951856</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48951856</guid></item><item><title><![CDATA[New comment by furkanturan in "Homomorphically encrypted CIFAR-10 inference in 200ms"]]></title><description><![CDATA[
<p>Indeed, we have our own library, Cyclops. We will share more about it soon :)</p>
]]></description><pubDate>Fri, 17 Jul 2026 20:16:06 +0000</pubDate><link>https://news.ycombinator.com/item?id=48951811</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=48951811</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48951811</guid></item><item><title><![CDATA[New comment by furkanturan in "Homomorphically encrypted CIFAR-10 inference in 200ms"]]></title><description><![CDATA[
<p>Thanks for taking the time and test it.<p>Key management is a critical part of the story, as j2kun has pointed out before. In this demo, we're intentionally caching the same key material for every visitor so we can showcase the actual FHE computation without making everyone wait through client-side key generation and upload. Even as a one-time cost, having each user generate their own keys in the browser and transfer them to the server introduces noticeable setup time, which we felt would get in the way of the experience for a demo. We actually implemented it that way at first; setup wasn't a matter of minutes, but we changed course after worrying about the experience for visitors on mobile data.<p>This demo is focused on demonstrating the computation itself, not a production-grade key management flow. Supporting per-user keys and a more realistic trust model is definitely on our roadmap for future demos.</p>
]]></description><pubDate>Fri, 17 Jul 2026 20:11:11 +0000</pubDate><link>https://news.ycombinator.com/item?id=48951764</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=48951764</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48951764</guid></item><item><title><![CDATA[New comment by furkanturan in "Homomorphically encrypted CIFAR-10 inference in 200ms"]]></title><description><![CDATA[
<p>Thanks a lot. Though encrypted AI chat bot is not far ahead, do not think of that as the primary target of today. Instead, there are many untapped applications (e.g. inter-bank transfers, industry, healthcare) where regulations, privacy laws and compliance requirements restrict institutions from touching data. These will initially be our key enablers, and over time we hope to extend the range of applications.</p>
]]></description><pubDate>Fri, 17 Jul 2026 19:13:59 +0000</pubDate><link>https://news.ycombinator.com/item?id=48951173</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=48951173</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48951173</guid></item><item><title><![CDATA[New comment by furkanturan in "Homomorphically encrypted CIFAR-10 inference in 200ms"]]></title><description><![CDATA[
<p>Consider encrypted AI. You ask a question under encryption. The remote calculates the answer, which is still under encryption. This is the critical point; the remote cannot see the question and answer. It only calculates. Once you receive the answer, you decrypt it and see the answer; only you see the answer.</p>
]]></description><pubDate>Fri, 17 Jul 2026 18:56:01 +0000</pubDate><link>https://news.ycombinator.com/item?id=48950972</link><dc:creator>furkanturan</dc:creator><comments>https://news.ycombinator.com/item?id=48950972</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48950972</guid></item></channel></rss>