I see a lot of comments here about how private AI is much cheaper than doing this, and that is currently true.

But I am curious if anyone can think of any use cases for this as it currently stands? For every idea I can think of, I can think of another solution that would probably be a better solution at current slowdown in cost from using homomorphic encryption.

For their two examples...

1) For the "will they click the add" one, how would anyone verify that the model I am uploading of myself is remotely accurate? And can't they learn my characteristics by running them against very specific ads that pinpoint my preferences?

2) For the "Do you have heart disease" one, why not just generating a billion datapoints (or more). The user can download the compressed data of this huge sample of points and then just enter their data on their own side and find the point that is closest (if not an exact match) to their own data?

I'm not pulling apart the idea of homorphic encryption, I'm just curious if anyone can think of a really compelling use case for it given it 10^3 to 10^6 greater compute cost?