{"id":41479,"date":"2025-11-12T17:00:00","date_gmt":"2025-11-12T17:00:00","guid":{"rendered":"https:\/\/www.prnasia.com\/story\/archive\/4821262_CN21262_0"},"modified":"2025-11-12T17:00:00","modified_gmt":"2025-11-12T17:00:00","slug":"desilo-and-cornami-announce-breakthrough-in-encrypted-ai-computation","status":"publish","type":"post","link":"https:\/\/transmediavictoria.net.au\/?p=41479","title":{"rendered":"DESILO and Cornami Announce Breakthrough in Encrypted AI Computation"},"content":{"rendered":"

The new method accelerates encrypted matrix multiplication, advancing practical fully homomorphic encryption (FHE) for AI<\/i><\/p>\n

SEOUL, South Korea<\/span>, Nov. 13, 2025 \/PRNewswire\/ — DESILO, a Privacy Enhancing Technology (PET) company, and Cornami, a leader in scalable compute acceleration, today announced new research that significantly improves the performance of encrypted AI computation using fully homomorphic encryption (FHE).<\/span><\/p>\n

The research paper \u2014 co-authored by Craig Gentry<\/span><\/b>, widely recognized as the father of FHE<\/i> and a G\u00f6del Prize laureate, and Yongwoo Lee<\/span><\/b>, Head of Cryptography at DESILO \u2014 introduces a new method designed for efficient encrypted matrix arithmetic<\/b>. According to results reported in the paper, the approach delivers up to 80\u00d7 faster encrypted matrix multiplication<\/b> compared to representative state-of-the-art baselines, marking a meaningful step toward in practical deployment of privacy-preserving AI.<\/p>\n

Matrix multiplication is the computational backbone of modern machine learning models. The new method is designed to provide an optimized pathway for encrypted<\/i> matrix multiplication across diverse scales and real-world workloads, narrowing the long-standing gap between theoretical cryptographic schemes and operational AI systems.<\/p>\n

“This research shows that privacy-preserving computation can be both efficient and practical,”<\/b> said Seungmyung Lee, CEO of DESILO<\/b>. “It forms a critical foundation for our encrypted AI stack, which enables organizations to analyze sensitive data without exposing it.”<\/i><\/p>\n

The work reflects the strategic collaboration between DESILO and Cornami, combining DESILO’s advances in FHE-based computation with Cornami’s high-performance compute architecture. The joint effort focuses on making FHE usable<\/b> in enterprise and AI environments where both data confidentiality<\/b> and computational efficiency<\/b> are essential.<\/p>\n

“For decades, Fully Homomorphic Encryption (FHE) has been the gold standard for data privacy, but its computational cost has made real-world use impractical,” said Dr. Craig Gentry<\/span><\/b>, Chief Scientist of Algorithms at Cornami<\/b>.  <\/i><\/p>\n

Our latest research focuses on accelerating encrypted matrix multiplication<\/b>, a fundamental operation representing over 90% of AI workloads. Combined with Cornami’s scalable Compute Fabric<\/b>, it delivers orders-of-magnitude faster<\/b> encrypted processing.”<\/i><\/p>\n

“This makes privacy-preserving AI<\/b> practical,” added Gentry. “Efficient, secure matrix multiplication enables Large Language Models to run inference on encrypted data with near-plaintext performance, strengthening compliance, sovereignty, and post-quantum security.”<\/i><\/p>\n

The research paper is now publicly available via the IACR ePrint Archive<\/a><\/b>.<\/a><\/p>\n

About DESILO
<\/b>DESILO develops encrypted computation and privacy-preserving AI infrastructure that enables multi-party data collaboration without exposing sensitive information. The company is expanding its presence in healthcare and finance, building multi-institutional collaboration models for areas such as credit scoring and multi-omics research.<\/p>\n

About Cornami
<\/b>Cornami is the developer of a massively parallel, next-generation compute fabric that delivers scalable performance for data-intensive workloads. Its architecture enables advanced encryption technologies \u2014 including Fully Homomorphic Encryption (FHE) \u2014 to run at practical speeds, making privacy-preserving AI viable across industries that demand both security and performance scale.<\/p>\n

Media Contact<\/b><\/p>\n

DESILO
<\/b>marketing@desilo.ai<\/a> <\/p>\n

Cornami
<\/b>media@cornami.com<\/a> <\/p>\n","protected":false},"excerpt":{"rendered":"

The new method accelerates encrypted matrix multiplication, advancing practical fully homomorphic encryption (FHE) for AI SEOUL, South Korea, Nov. 13, 2025 \/PRNewswire\/ — DESILO, a Privacy Enhancing Technology (PET) company, and Cornami, a leader in scalable compute acceleration, today announced new research that significantly improves the performance of encrypted AI computation using fully homomorphic encryption (FHE). 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