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mmaundertoday at 2:37 PM1 replyview on HN

Someone explain how you'd create a vector embedding using homomorphically encrypted data, without decrypting it. Seems like a catch 22. You don't get to know the semantic meaning, but need the semantic meaning to position it in high dimensional space. I guess the point I'm making is that sure, you can sell compute for FHE, but you quickly run up against a hard limit on any value added SaaS you can provide the customer. This feels like a solution that's being shoehorned in because cloud providers really really really want to have a customer use their data center, when in truth the best solution would be a secure facility for the customer so that applications can actually understand the data they're working with.


Replies

bob1029today at 3:57 PM

Most of modern machine learning is effectively linear algebra. We can achieve semantic search over encrypted vectors if the encryption relies on similar principles.