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.
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.
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.
“Cornami’s unique and scalable computational fabric is the first compute platform we’ve seen that can meet FHE’s substantial computing requirements hitting both performance and cost requirements for a commercially viable product.
“We look forward to working with Cornami to provide unrestricted FHE solutions to the market, said 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.
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.
“In order to achieve value from data analytics, including those driving the AI and ML market, the industry needs to address the protection of the data.
“Massive security breaches bring regulatory pressure to restrict data collection and cloud-based information with the realization that regardless of effort, with existing perimeter-based security measures, computing environment attack points will always exist.
“The cryptography experts at Inpher have developed quantum-safe cryptographic primitives and protocols to encrypt data and perform arbitrary computation on that encrypted data that returns an encrypted result.
“Combining both Cornami and Inpher technologies will enable future-proof and provable data security. These are mathematical computationally-intensive algorithms highly unsuitable for the processors of today.
“We greatly value our partnership with Inpher to enable for the first time the performance required to deliver commercially viable FHE products in real-time, said Paul Master, CTO and co-founder of Cornam.”
Cornami and Inpher have completed several technical exploratory milestones to understand their harmonious fit. Both companies are engaged in delivering market ready solutions that address multiple customers’ needs to compute on encrypted datasets.