{"id":43690,"date":"2026-03-08T01:35:00","date_gmt":"2026-03-08T01:35:00","guid":{"rendered":"http:\/\/www.prnasia.com\/story\/archive\/4903468_CN03468_0"},"modified":"2026-03-08T01:35:00","modified_gmt":"2026-03-08T01:35:00","slug":"desilo-and-fhe-inventor-craig-gentry-introduce-5th-generation-gl-fhe-scheme-for-private-ai","status":"publish","type":"post","link":"https:\/\/transmediavictoria.net.au\/?p=43690","title":{"rendered":"DESILO and FHE Inventor Craig Gentry Introduce 5th-Generation &#8220;GL&#8221; FHE Scheme for Private AI"},"content":{"rendered":"<br \/>\n<table name=\"logo_release\" border=\"0\" cellspacing=\"10\" cellpadding=\"5\" align=\"right\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" src=\"https:\/\/mma.prnasia.com\/media2\/2928224\/DESILO_LOGO_Greaan_Wide_Logo.jpg?p=medium600\" border=\"0\" alt=\"\" title=\"logo\" hspace=\"0\" vspace=\"0\" width=\"118\" \/><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><b>Debuting at the FHE.org 2026 Conference, the Gentry\u2013Lee (GL) scheme introduces a breakthrough in matrix multiplication performance for Fully Homomorphic Encryption.<\/b><\/p>\n<p><span class=\"legendSpanClass\"><span class=\"xn-location\">SEOUL, South Korea<\/span> and <span class=\"xn-location\">TAIPEI<\/span><\/span>, <span class=\"legendSpanClass\"><span class=\"xn-chron\">March 8, 2026<\/span><\/span> \/PRNewswire\/ &#8212;&nbsp;DESILO, a deep-tech company specializing in privacy-enhancing technologies, today unveiled the <b>Gentry\u2013Lee (GL) scheme<\/b>, a major advancement positioned as the <b>5th generation of Fully Homomorphic Encryption (FHE)<\/b>.<\/p>\n<div class=\"PRN_ImbeddedAssetReference\" id=\"DivAssetPlaceHolder1\">\n<\/p><\/div>\n<p>The scheme is being formally presented at the <b>FHE.org 2026 Conference in <span class=\"xn-location\">Taipei<\/span><\/b>. The framework is co-authored by <b><span class=\"xn-person\">Yongwoo Lee<\/span> (Chief Scientist, DESILO)<\/b> and <b><span class=\"xn-person\">Craig Gentry<\/span> (Chief Scientist, Cornami)<\/b> \u2014 the inventor of FHE in 2009 and recipient of the 2022 G&ouml;del Prize.<\/p>\n<p>As organizations increasingly adopt AI while handling sensitive data, a new paradigm known as <b>Private AI<\/b> is emerging. Private AI enables AI systems to operate directly on <b>encrypted data<\/b>, allowing enterprises and organizations to use AI without exposing sensitive prompts, inputs, or model outputs to the AI provider.<\/p>\n<p>This capability is particularly important for <b>highly regulated industries, enterprise environments with strict data protection and sovereignty requirements, and organizations working with highly sensitive or valuable data<\/b>.<\/p>\n<p><b>Fully Homomorphic Encryption (FHE)<\/b> enables this approach by allowing computations to be performed directly on encrypted data without decrypting it.<\/p>\n<p>While earlier FHE schemes made encrypted computation increasingly practical, their computational overhead has remained a major challenge for modern AI workloads. The <b>GL scheme introduces a new architecture designed to significantly improve the efficiency of matrix multiplication<\/b>, one of the core operations underlying modern deep learning systems.<\/p>\n<p>Because <b>Large Language Models (LLMs)<\/b> rely heavily on repeated matrix multiplication, improving this operation is critical for enabling AI computation under encryption.<\/p>\n<p>&quot;Back in the early days of FHE, the primary goal was proving mathematical feasibility,&quot; said <b><span class=\"xn-person\">Craig Gentry<\/span>, Chief Scientist of Cornami<\/b>. &quot;With the GL scheme, we are fundamentally restructuring how homomorphic operations handle matrix multiplication. Optimizing these core operations brings encrypted computation much closer to supporting modern AI architectures.&quot;<\/p>\n<p>&quot;Matrix multiplication is the dominant workload in modern AI systems,&quot; said <b><span class=\"xn-person\">Yongwoo Lee<\/span>, Chief Scientist of DESILO<\/b>. &quot;With the GL scheme, we introduce a new framework designed to significantly improve how these operations are performed under homomorphic encryption, bringing practical Private AI closer to reality.&quot;<\/p>\n<p>The full research paper describing the GL scheme is available via the <b>IACR ePrint archive<\/b>.<\/p>\n<p>Technical paper:<br \/><u><a href=\"https:\/\/eprint.iacr.org\/2025\/1935\" target=\"_blank\" rel=\"nofollow noopener\" style=\"color: #0000FF\">https:\/\/eprint.iacr.org\/2025\/1935<\/a><\/u><\/p>\n<p>FHE.org 2026 Conference:<br \/><u><a href=\"https:\/\/fhe.org\/conferences\/conference-2026\/\" target=\"_blank\" rel=\"nofollow noopener\" style=\"color: #0000FF\">https:\/\/fhe.org\/conferences\/conference-2026\/<\/a><\/u><\/p>\n<p>Learn more about DESILO and Private AI:<br \/><u><a href=\"https:\/\/desilo.ai\" target=\"_blank\" rel=\"nofollow noopener\" style=\"color: #0000FF\">https:\/\/desilo.ai<\/a><\/u><\/p>\n<p><b>Media Contact<br \/><\/b><span class=\"xn-person\">Howard Park<\/span><br \/>Co-founder \/ CSO<br \/><a href=\"mailto:contact@desilo.ai\" target=\"_blank\" rel=\"nofollow noopener\" style=\"color: #0000FF\">contact@desilo.ai<\/a><\/p>\n<p><b>About DESILO<\/b><\/p>\n<p>DESILO is a deep-tech company advancing the future of <b>Private AI<\/b> through breakthroughs in cryptography. Specializing in <b>Fully Homomorphic Encryption (FHE)<\/b> and privacy-enhancing technologies, DESILO develops infrastructure that enables AI systems to operate securely on encrypted data, allowing organizations to unlock the value of sensitive data without compromising privacy.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Debuting at the FHE.org 2026 Conference, the Gentry\u2013Lee (GL) scheme introduces a breakthrough in matrix multiplication performance for Fully Homomorphic Encryption. SEOUL, South Korea and TAIPEI, March 8, 2026 \/PRNewswire\/ &#8212;&nbsp;DESILO, a deep-tech company specializing in privacy-enhancing technologies, today unveiled the Gentry\u2013Lee (GL) scheme, a major advancement positioned as the 5th generation of Fully Homomorphic [&hellip;]<\/p>\n","protected":false},"author":8,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-43690","post","type-post","status-publish","format-standard","hentry","category-business-news"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>DESILO and FHE Inventor Craig Gentry Introduce 5th-Generation &quot;GL&quot; FHE Scheme for Private AI - My Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/transmediavictoria.net.au\/?p=43690\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DESILO and FHE Inventor Craig Gentry Introduce 5th-Generation &quot;GL&quot; FHE Scheme for Private AI - My Blog\" \/>\n<meta property=\"og:description\" content=\"Debuting at the FHE.org 2026 Conference, the Gentry\u2013Lee (GL) scheme introduces a breakthrough in matrix multiplication performance for Fully Homomorphic Encryption. 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