| General | |
|---|---|
| Derived from | Various assumptions, including learning with errors, Ring learning with errors or even RSA (multiplicative) and others |
| Related to | Functional encryption |
Homomorphic encryption is a form of encryption that allows computations to be performed on encrypted data without first having to decrypt it. The resulting computations are left in an encrypted form which, when decrypted, result in an output that is identical to that produced had the operations been performed on the unencrypted data. Homomorphic encryption can be used for privacy-preserving outsourced storage and computation. This allows data to be encrypted and outsourced to commercial cloud environments for processing, all while encrypted.
Homomorphic encryption eliminates the need for processing data in the clear, thereby preventing attacks that would enable an attacker to access that data while it is being processed, using privilege escalation.[1]
For sensitive data, such as healthcare information, homomorphic encryption can be used to enable new services by removing privacy barriers inhibiting data sharing or increasing security to existing services. For example, predictive analytics in healthcare can be hard to apply via a third-party service provider due to medical data privacy concerns. But if the predictive-analytics service provider could operate on encrypted data instead, without having the decryption keys, these privacy concerns are diminished. Moreover, even if the service provider's system is compromised, the data would remain secure.[2]