{"id":33450,"date":"2025-09-16T07:26:41","date_gmt":"2025-09-16T07:26:41","guid":{"rendered":"https:\/\/www.hotelsalepage.com\/feed\/cision-pr-newswire\/cornami-and-desilo-bring-encrypted-ai-to-scale-with-deployable-fhe-based-llm\/"},"modified":"2025-09-16T07:26:41","modified_gmt":"2025-09-16T07:26:41","slug":"cornami-and-desilo-bring-encrypted-ai-to-scale-with-deployable-fhe-based-llm-2","status":"publish","type":"post","link":"https:\/\/thaipropertynews.com\/feeds\/?p=33450","title":{"rendered":"Cornami and DESILO Bring Encrypted AI to Scale with Deployable FHE-Based LLM"},"content":{"rendered":"

SAN JOSE, Calif.<\/span><\/span>, Sept. 16, 2025<\/span><\/span> \/PRNewswire\/ — \u00a0At the AI Infra Summit 2025, Cornami, a leader in scalable computing\u00a0architectures, and DESILO, a privacy-enhancing technology (PET) startup, announced the\u00a0deployment of a fully homomorphic encryption (FHE) based large language model (LLM).\u00a0The solution processes sensitive data while it remains encrypted, delivering real-world speed\u00a0and accuracy.<\/p>\n

\n

\"\"<\/a>
<\/span><\/p>\n<\/div>\n

Breaking the Privacy vs. Performance Dilemma<\/b><\/p>\n

From healthcare to financial services, enterprises have faced the same tradeoff: stronger data protection typically slows AI performance, while faster insights often compromise security. This collaboration addresses the long-standing dilemma by enabling encrypted AI\u00a0inference at near plaintext speeds.<\/p>\n

“For decades, FHE was considered too slow for real deployment. By accelerating encrypted computation, we are proving that enterprises can have both privacy and performance.<\/span>” <\/span>Dr. Craig Gentry<\/span>, Chief Scientist of Algorithms for Cornami, also known as the father of FHE, has been leading key innovations to provide unique value to this industry for Cornami and its software partners. One such innovation is plaintext ciphertext matrix multiplication (PCMM) which affects\u00a090%+ of LLM compute<\/b>. PCMM allows these operations to be executed securely with extremely low overhead. This innovation together with scalable acceleration using\u00a0Cornami’s Compute Fabric results in\u00a0orders-of-magnitude faster<\/b>\u00a0performance over traditional encrypted compute approaches. “The value of PCMM is that it makes privacy-preserving AI practical by<\/i> enabling matrix multiplication \u2014 the core of LLM compute \u2014 to be executed<\/i> efficiently and securely, closing the performance gap between fully encrypted<\/i> and plaintext LLM inference computation while strengthening compliance,<\/i> sovereignty, and post-quantum security,” <\/span><\/i>stated Dr. Craig Gentry<\/span>.<\/p>\n

Extending Encrypted AI Across Industries<\/b><\/p>\n

Healthcare offers a clear use case. Clinical trials generate vast amounts of imaging and\u00a0patient data that must remain secure, yet timely insights are critical. With this breakthrough,\u00a0researchers can analyze encrypted records without exposing sensitive information.<\/p>\n

Beyond healthcare, the same principles apply to finance, government, and cloud services\u00a0where compliance with zero-trust and data sovereignty requirements is non-negotiable.Seungmyung Lee, CEO of DESILO, commented, “Our principle has always been simple:never decrypt. With Cornami, we are making that principle practical. This collaboration lays the foundation for enterprises to safely unlock high-value data, and it aligns with the upcoming launch of our HARVEST platform, which will support global healthcare partners.”<\/span><\/p>\n

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

Cornami delivers breakthrough performance for scalable computing, enabling advanced\u00a0encryption technologies like FHE to operate at speeds previously unattainable. Its architecture is\u00a0designed to accelerate privacy-preserving AI across industries that demand both security\u00a0and scale.<\/p>\n

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

DESILO is a Korea-based PET startup scaling globally with deployments across Asia<\/span> and\u00a0upcoming pilots in North America<\/span> and Europe<\/span>. The company develops secure collaboration\u00a0solutions using FHE and federated learning, ensuring sensitive data never leaves encrypted\u00a0form. Its flagship HARVEST platform, launching this December, accelerates secure\u00a0multi-party data collaboration in healthcare and beyond.<\/p>\n

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

<\/p>\n

SAN JOSE, Calif.<\/span><\/span>, Sept. 16, 2025<\/span><\/span> \/PRNewswire\/ — \u00a0At the AI Infra Summit 2025, Cornami, a leader in scalable computing\u00a0architectures, and DESILO, a privacy-enhancing technology (PET) startup, announced the\u00a0deployment of a fully homomorphic encryption (FHE) based large language model (LLM).\u00a0The solution processes sensitive data while it remains encrypted, delivering real-world speed\u00a0and accuracy.<\/p>\n

\n

\"\"<\/a>
<\/span><\/p>\n<\/div>\n

Breaking the Privacy vs. Performance Dilemma<\/b><\/p>\n

From healthcare to financial services, enterprises have faced the same tradeoff: stronger data protection typically slows AI performance, while faster insights often compromise security. This collaboration addresses the long-standing dilemma by enabling encrypted AI\u00a0inference at near plaintext speeds.<\/p>\n

“For decades, FHE was considered too slow for real deployment. By accelerating encrypted computation, we are proving that enterprises can have both privacy and performance.<\/span>” <\/span>Dr. Craig Gentry<\/span>, Chief Scientist of Algorithms for Cornami, also known as the father of FHE, has been leading key innovations to provide unique value to this industry for Cornami and its software partners. One such innovation is plaintext ciphertext matrix multiplication (PCMM) which affects\u00a090%+ of LLM compute<\/b>. PCMM allows these operations to be executed securely with extremely low overhead. This innovation together with scalable acceleration using\u00a0Cornami’s Compute Fabric results in\u00a0orders-of-magnitude faster<\/b>\u00a0performance over traditional encrypted compute approaches. “The value of PCMM is that it makes privacy-preserving AI practical by<\/i> enabling matrix multiplication \u2014 the core of LLM compute \u2014 to be executed<\/i> efficiently and securely, closing the performance gap between fully encrypted<\/i> and plaintext LLM inference computation while strengthening compliance,<\/i> sovereignty, and post-quantum security,” <\/span><\/i>stated Dr. Craig Gentry<\/span>.<\/p>\n

Extending Encrypted AI Across Industries<\/b><\/p>\n

Healthcare offers a clear use case. Clinical trials generate vast amounts of imaging and\u00a0patient data that must remain secure, yet timely insights are critical. With this breakthrough,\u00a0researchers can analyze encrypted records without exposing sensitive information.<\/p>\n

Beyond healthcare, the same principles apply to finance, government, and cloud services\u00a0where compliance with zero-trust and data sovereignty requirements is non-negotiable.Seungmyung Lee, CEO of DESILO, commented, “Our principle has always been simple:never decrypt. With Cornami, we are making that principle practical. This collaboration lays the foundation for enterprises to safely unlock high-value data, and it aligns with the upcoming launch of our HARVEST platform, which will support global healthcare partners.”<\/span><\/p>\n

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

Cornami delivers breakthrough performance for scalable computing, enabling advanced\u00a0encryption technologies like FHE to operate at speeds previously unattainable. Its architecture is\u00a0designed to accelerate privacy-preserving AI across industries that demand both security\u00a0and scale.<\/p>\n

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

DESILO is a Korea-based PET startup scaling globally with deployments across Asia<\/span> and\u00a0upcoming pilots in North America<\/span> and Europe<\/span>. The company develops secure collaboration\u00a0solutions using FHE and federated learning, ensuring sensitive data never leaves encrypted\u00a0form. Its flagship HARVEST platform, launching this December, accelerates secure\u00a0multi-party data collaboration in healthcare and beyond.<\/p>\n

<\/div>\n

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