[eyegor]

You're protecting your inputs and outputs. If you have a model that's designed to run with sensitive data but you want to don't have the compute power to run it locally, what do you do? Putting the model on a cloud provider means their system would see your sensitive data, which may be unacceptable for contractual or legal reasons. This lets you send the inputs encrypted, receive the outputs encrypted, then you can decrypt the outputs in your weak but trusted environment.

[Nullabillity]

That sounds like fairy tale engineering. Even for the IoT-with-a-potato-MCU use case, you'd be much better off offloading that computation to a trustable device (such as the user's desktop computer or home gateway) instead of shipping it off a cloud environment and paying the (absolutely massive) FHE tax.

[fd0r]

In a case where you are offloading the computation to another device because of compute limitations it would indeed probably make more sense, at least at the moment, to offload the computation to a trusted device.

But there is always the case where the server side with the model does not want to disclose the model itself while the client does not want to disclose its data either (like in many healthcare applications for example or in the case of the recent Open ai Samsung incident). In this case the FHE tax might be a decent price to pay.

If you want to read more on the topic, there is blog post about the cost of running a LLM in FHE: https://www.zama.ai/post/chatgpt-privacy-with-homomorphic-en...

The main improvements in terms of speed will come from dedicated hardware accelerators but some models (those that run on tabular data for example) already have acceptable runtimes.

[bczm]

Sure, sometimes, if you have a trusted device, great. However, in other use cases, there will be no device which is both trusted by the user _and_ the model owner, and FHE will help here. We have to remind how valuable the models are

[Nullabillity]

Too bad for the model owner, then.