IBM completes successful field trials on Fully Homomorphic Encryption (Link)

FHE is a type of encryption that allows direct mathematical operations on the encrypted data. Upon decryption, the results will be correct. For example, you might encrypt 2, 3, and 7 and send the three encrypted values to a third party. If you then ask the third party to add the first and second values, then multiply the result by the third value and return the result to you, you can then decrypt that result—and get 35.

You don't ever have to share a key with the third party doing the computation; the data remains encrypted with a key the third party never received. So, while the third party performed the operations you asked it to, it never knew the values of either the inputs or the output. You can also ask the third party to perform mathematical or logical operations of the encrypted data with non-encrypted data—for example, in pseudocode, FHE_decrypt(FHE_encrypt(2) * 5) equals 10.

Although Fully Homomorphic Encryption makes things possible that otherwise would not be, it comes at a steep cost. Above, we can see charts indicating the additional compute power and memory resources required to operate on FHE-encrypted machine-learning models—roughly 40 to 50 times the compute and 10 to 20 times the RAM that would be required to do the same work on unencrypted models.

As daunting as the performance penalties for FHE may be, they're well under the threshold for usefulness—Bergamaschi told us that IBM initially estimated that the minimum efficiency to make FHE useful in the real world would be on the order of 1,000:1. With penalties well under 100:1, IBM contracted with one large American bank and one large European bank to perform real-world field trials of FHE techniques, using live data.