Fully Homomorphic Encryption (FHE), commonly also referred to as Homomorphic Encryption, is arguably the first and primary breakthrough in theoretical computer science of the 21st century. For a long time people would use encryption technologies to protect their data. But if the data was encrypted, whoever holds the data is not really able to do much with it, other than give it in encrypted form to someone else.

Homomorphic encryption provides a way of encrypting data so you can give the encrypted data to someone you don’t necessarily trust – like on a public cloud – and then have the cloud run computations on the encrypted data without sharing keys, without giving any access to the encrypted data or risking leakage of sensitive data. That cloud host would run a computation on the data, get an encrypted result, give the result back to you. You could then decrypt that result, and that decrypted result is the same as if you had run the original source computation on the original data without encryption.

So, in some sense, FHE is a fundamental black magic, in that you can actually enable computing on encrypted data. This has tremendous implications for multiple industries, for government, and for the society at large. Particularly, let’s look at the healthcare industry. In healthcare there are large issues of privacy. All this data is being generated by care providers treating patients across the world, and a lot of this data needs to be shared with insurance providers or other caregivers. There are both very important privacy concerns associated with patients not wanting to share that data, and also very strong commercial competitive concerns, where insurance companies don’t want to share information about their subscribers and give their competitors any kind of information about who they are receiving their money from. At the same time, there would be a large benefit if researchers, particularly with funding with the NIH, VA, FDA or other agencies, could get access to broader sets of patient data to run computations on those data to compute, for example – what is the most beneficial treatment under certain observed symptoms? Or what is the most effective drug to prescribe to a patient under certain symptoms to maximize patient outcomes, minimize patient death, and for overall betterment of patient health?

The trouble with this is that because the patient, the insurance companies, the hospitals and care providers have these very strong privacy concerns – and very valid privacy concerns – they are less willing to share the data. With homomorphic encryption, we provide a way where patients, doctors, insurance agents could encrypt data, share the data, and enable computations on this data to provide better healthcare for their patients. Duality has been able to mature homomorphic encryption so that it is not just theoretically feasible, but it is now practical.