3 ms·
Prompt Privacy from LLMs
- napping_penguin 2mo agoI recently came across a really interesting piece of privacy technology. Suppose you have a model M and a prompt P. The technique allows you to create an obfuscated prompt Q such that: - M(Q) is nearly the same as M(P) - P is hard to reverse engineer from Q As a applied crypto researcher, this feels like an "ML-based homomorphic encryption". Works with any model (that supports prompt_embeds) without changing anything on the model side. Very cool indeed. Credit note: This method was invented by Protopia Labs and I don't have any affiliation there.
- tmpsvc2695f5 2mo ago[flagged]
- tmpsvc2695f5 2mo ago[flagged]