{"id":50318,"date":"2026-03-08T08:35:00","date_gmt":"2026-03-08T01:35:00","guid":{"rendered":"https:\/\/thaipropertynews.com\/feeds\/?p=50318"},"modified":"2026-03-08T08:35:00","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:\/\/thaipropertynews.com\/feeds\/?p=50318","title":{"rendered":"DESILO and FHE Inventor Craig Gentry Introduce 5th-Generation “GL” FHE Scheme for Private AI"},"content":{"rendered":"\n\n\n
\"\"<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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

SEOUL, South Korea<\/span> and TAIPEI<\/span><\/span>, March 8, 2026<\/span><\/span> \/PRNewswire\/ —\u00a0DESILO, a deep-tech company specializing in privacy-enhancing technologies, today unveiled the Gentry\u2013Lee (GL) scheme<\/b>, a major advancement positioned as the 5th generation of Fully Homomorphic Encryption (FHE)<\/b>.<\/p>\n

\n<\/div>\n

The scheme is being formally presented at the FHE.org 2026 Conference in Taipei<\/span><\/b>. The framework is co-authored by Yongwoo Lee<\/span> (Chief Scientist, DESILO)<\/b> and Craig Gentry<\/span> (Chief Scientist, Cornami)<\/b> \u2014 the inventor of FHE in 2009 and recipient of the 2022 G\u00f6del Prize.<\/p>\n

As organizations increasingly adopt AI while handling sensitive data, a new paradigm known as Private AI<\/b> is emerging. Private AI enables AI systems to operate directly on encrypted data<\/b>, allowing enterprises and organizations to use AI without exposing sensitive prompts, inputs, or model outputs to the AI provider.<\/p>\n

This capability is particularly important for highly regulated industries, enterprise environments with strict data protection and sovereignty requirements, and organizations working with highly sensitive or valuable data<\/b>.<\/p>\n

Fully Homomorphic Encryption (FHE)<\/b> enables this approach by allowing computations to be performed directly on encrypted data without decrypting it.<\/p>\n

While earlier FHE schemes made encrypted computation increasingly practical, their computational overhead has remained a major challenge for modern AI workloads. The 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

Because Large Language Models (LLMs)<\/b> rely heavily on repeated matrix multiplication, improving this operation is critical for enabling AI computation under encryption.<\/p>\n

“Back in the early days of FHE, the primary goal was proving mathematical feasibility,” said Craig Gentry<\/span>, Chief Scientist of Cornami<\/b>. “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.”<\/p>\n

“Matrix multiplication is the dominant workload in modern AI systems,” said Yongwoo Lee<\/span>, Chief Scientist of DESILO<\/b>. “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.”<\/p>\n

The full research paper describing the GL scheme is available via the IACR ePrint archive<\/b>.<\/p>\n

Technical paper:
https:\/\/eprint.iacr.org\/2025\/1935<\/a><\/u><\/p>\n

FHE.org 2026 Conference:
https:\/\/fhe.org\/conferences\/conference-2026\/<\/a><\/u><\/p>\n

Learn more about DESILO and Private AI:
https:\/\/desilo.ai<\/a><\/u><\/p>\n

Media Contact
<\/b>Howard Park<\/span>
Co-founder \/ CSO
contact@desilo.ai<\/a><\/p>\n

About DESILO<\/b><\/p>\n

DESILO is a deep-tech company advancing the future of Private AI<\/b> through breakthroughs in cryptography. Specializing in 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

\u00a0<\/p>","protected":false},"excerpt":{"rendered":"

<\/p>\n\n\n\n
\"\"<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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

SEOUL, South Korea<\/span> and TAIPEI<\/span><\/span>, March 8, 2026<\/span><\/span> \/PRNewswire\/ —\u00a0DESILO, a deep-tech company specializing in privacy-enhancing technologies, today unveiled the Gentry\u2013Lee (GL) scheme<\/b>, a major advancement positioned as the 5th generation of Fully Homomorphic Encryption (FHE)<\/b>.<\/p>\n

\n<\/div>\n

The scheme is being formally presented at the FHE.org 2026 Conference in Taipei<\/span><\/b>. The framework is co-authored by Yongwoo Lee<\/span> (Chief Scientist, DESILO)<\/b> and Craig Gentry<\/span> (Chief Scientist, Cornami)<\/b> \u2014 the inventor of FHE in 2009 and recipient of the 2022 G\u00f6del Prize.<\/p>\n

As organizations increasingly adopt AI while handling sensitive data, a new paradigm known as Private AI<\/b> is emerging. Private AI enables AI systems to operate directly on encrypted data<\/b>, allowing enterprises and organizations to use AI without exposing sensitive prompts, inputs, or model outputs to the AI provider.<\/p>\n

This capability is particularly important for highly regulated industries, enterprise environments with strict data protection and sovereignty requirements, and organizations working with highly sensitive or valuable data<\/b>.<\/p>\n

Fully Homomorphic Encryption (FHE)<\/b> enables this approach by allowing computations to be performed directly on encrypted data without decrypting it.<\/p>\n

While earlier FHE schemes made encrypted computation increasingly practical, their computational overhead has remained a major challenge for modern AI workloads. The 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

Because Large Language Models (LLMs)<\/b> rely heavily on repeated matrix multiplication, improving this operation is critical for enabling AI computation under encryption.<\/p>\n

“Back in the early days of FHE, the primary goal was proving mathematical feasibility,” said Craig Gentry<\/span>, Chief Scientist of Cornami<\/b>. “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.”<\/p>\n

“Matrix multiplication is the dominant workload in modern AI systems,” said Yongwoo Lee<\/span>, Chief Scientist of DESILO<\/b>. “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.”<\/p>\n

The full research paper describing the GL scheme is available via the IACR ePrint archive<\/b>.<\/p>\n

Technical paper:
https:\/\/eprint.iacr.org\/2025\/1935<\/a><\/u><\/p>\n

FHE.org 2026 Conference:
https:\/\/fhe.org\/conferences\/conference-2026\/<\/a><\/u><\/p>\n

Learn more about DESILO and Private AI:
https:\/\/desilo.ai<\/a><\/u><\/p>\n

Media Contact
<\/b>Howard Park<\/span>
Co-founder \/ CSO
contact@desilo.ai<\/a><\/p>\n

About DESILO<\/b><\/p>\n

DESILO is a deep-tech company advancing the future of Private AI<\/b> through breakthroughs in cryptography. Specializing in 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

\u00a0<\/p>\n

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