Homomorphic encryption
Compute on data while it stays encrypted — analyse what you are never allowed to see.
In one line
Homomorphic encryption (HE) lets a server run computations directly on ciphertext: it returns an encrypted result that only the data owner can decrypt, having learned nothing along the way.
How it works
Schemes support addition and multiplication on encrypted values; "fully" homomorphic schemes (FHE — BGV/BFV/CKKS lineages) support arbitrary circuits, with CKKS handling the approximate real-number math that statistics and ML need. The catch is cost: operations are orders of magnitude slower than plaintext, so practical systems use HE surgically — for one sensitive scoring step — or hybrid designs with secure enclaves and multi-party computation.
Where it shows up in digital health
Outsourcing analysis of genomic or claims data to an untrusted cloud; cross-institution statistics where even the aggregator must not see inputs; encrypted inference (a model scores an encrypted record). Today it is a research-and-niche tool gaining ground — worth knowing as the endpoint of the privacy-tech spectrum that starts at TLS and passes through federated learning.