Homomorphic Encryption

Homomorphic Encryption, abbreviated HE, is the family of cryptographic schemes that allow computations on encrypted data to produce encrypted results that, when decrypted, match the result of operating on the plaintext — enabling third parties to compute over your data without ever seeing it in the clear. The concept was theorized by Rivest, Adleman, and Dertouzos in 1978; the first fully homomorphic scheme (FHE, supporting both addition and multiplication of arbitrary depth) was constructed by Craig Gentry in 2009. Modern HE schemes fall into three families: partially homomorphic (PHE, one operation only, e.g., RSA for multiplication, Paillier for addition); somewhat homomorphic (SHE, both operations but bounded depth); and fully homomorphic (FHE, arbitrary depth via bootstrapping). The leading FHE schemes today are BGV, BFV, CKKS (the practical workhorse for real-number computation, used in privacy-preserving ML), and TFHE (fast Boolean circuit evaluation). Open-source libraries include Microsoft SEAL, IBM HElib, OpenFHE (the open-source successor merging PALISADE, HElib, and HEAAN), Concrete (TFHE-based, Python-friendly, from Zama), and TenSEAL (CKKS for tensors). The honest assessment: HE is 100x-10,000x slower than plaintext computation, so it is reserved for high-value scenarios where privacy outweighs the performance cost — encrypted inference on sensitive inputs (medical, financial), privacy-preserving collaborative analytics, secure outsourcing of computation to untrusted clouds. For LLMs specifically, full HE inference is currently impractical for frontier-scale models, but research is active on hybrid approaches (HE for sensitive portions, plaintext for the rest) and on smaller specialty models. AI governance teams in regulated industries (healthcare, finance, defense) explore HE for scenarios where data sovereignty is paramount and the latency/cost tradeoff is acceptable.

Privacy beyond the perimeter, building on 25 years of on-premise discipline: Centralpoint's foundation in on-premise deployment means clients have already won the easy privacy battle — HE is a tool for the harder cases where computation must happen on untrusted infrastructure. The 25-year discipline of audit-grade key management and access control extends naturally to HE keys. HE runs on-premise, tokens meter per skill, and HE-augmented chatbots deploy through one line of JavaScript.


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