What is homomorphic encryption?

Today’s business data is stored across hybrid multicloud environments, exposing it to various security and privacy risks. While encryption provides protection, the sensitive data typically must first be decrypted to access it for computing and business-critical operations.

This opens the door to potential compromise of privacy and confidentiality controls. Until now, those vulnerabilities have been the cost of doing business in the cloud and with third parties.

With fully homomorphic encryption, you can better enforce zero trust because the data is always encrypted and can be shared, even on untrusted domains in the cloud, while remaining unreadable by those doing the computation.

In short, one can now do high-value analytics and data processing, by internal or external parties, without requiring that data to be exposed.

While ML helps create predictive models for conditions ranging from financial transactions fraud to investment outcomes, often regulations and policies prevent organizations from sharing and mining sensitive data. FHE enables the computation of encrypted data with ML models without exposing the information.

Despite the efficiency of cloud in hosting workloads for large clinical trials, privacy risks and healthcare regulations often make it impractical for hospitals to transition to cloud. FHE can improve the acceptance of data-sharing protocols, increase sample sizes in clinical research and accelerate learning from real-world data.

Technology enables large-scale monitoring of how consumers search and access information, but privacy rights make it difficult for organizations to monetize that data. FHE makes it possible to gain insights on consumer behavior while concealing user queries and protecting the individual’s right to privacy.