Cornami and Inpher announced today their partnership to collaborate on delivering commercially viable Fully Homomorphic Encryption (FHE) functionality to the market. FHE has long been described as transformative for data privacy and cloud security as it enables computing on encrypted data sets, thereby keeping the underlying data secure. However, existing FHE algorithms are computationally intensive and have been often considered as not yet practical for real world applications. Cornami’s partnership with Inpher overcomes such limitations to deliver real-time FHE computing to a ready and rapidly expanding market.
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Inpher is a cryptographic Secret Computing company that powers privacy-preserving AI and analytics. Secret Computing has brought years of academic research in secure Multi-Party Computation (MPC) and FHE — into commercially-ready applications. With Secret Computing technology, data scientists can finally build advanced analytics and Artificial Intelligence (AI) models on distributed data sources without ever exposing or transferring sensitive data across departments, organizations, or jurisdictions.n
Secret Computing ensures that data privacy and security are retained under the privacy requirements of organizations during these computations, including being GDPR compliant. Cornami has developed a break-through computing-hardware architecture that massively scales application performance without penalties to deliver real-time computing for critical and complex applications that are out of reach of traditional, legacy processors. The Cornami technology reduces power and latency while vastly increasing the compute performance for today’s immense datasets requiring real-time computing.
Dimitar Jetchev, CTO and co-founder of Inpher, Inc.
The two companies are collaborating to enable Inpher’s advanced cryptographic products, in which data remains protected while being processed, to execute on the Cornami computing platform. Cornami’s unique next-generation post-von Neumann architecture enables massive parallelism and pipelining capabilities at scale, delivering compute-intensive FHE for commercially viable real-time products and services.
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FHE Brings Three Significant Sea-Change Advances to the Information Processing Industry:
Massive and Costly Data Breaches become a thing of the Past
Commercially viable FHE provides quantum-secure privacy-preserving computing on encrypted data sets, keeping the underlying data secure. In essence, assume that computing environments are compromised; so, secure the data. The data, including its unrestricted computational derivatives, remain encrypted at rest and throughout its processing life cycle. Data is only decrypted to plaintext in data owner-controlled environments.
Data Secure from Quantum Computer-Based Attacks
FHE is based on notoriously hard mathematical lattice problems that are secure against attacks even by quantum computers. This feature allows provable data security – even in untrusted computing environments like public cloud platforms.
AI Training, AI Inference, and Analytics Gain Data Insights without Data Exposure
Datasets from single or multiple sources can be encrypted using the same public FHE key. The data is then aggregated into an encrypted database, where AI (both training and inference) and analytic algorithms can be applied at commercially viable speeds. Results can be decrypted by a third-party without ever exposing plaintext data. This feature enables the cooperation of multiple, independent organizations such as healthcare companies, governments, financial institutions and more to gain insights into their collective data while preserving the privacy of that data’s contents.
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