That 90% only measures images already known to be one of CIFAR-10’s ten classes. There is no “none of the above,” so even a perfect benchmark score would still force a calculator into one of those labels.
Consider encrypted AI. You ask a question under encryption. The remote calculates the answer, which is still under encryption. This is the critical point; the remote cannot see the question and answer. It only calculates. Once you receive the answer, you decrypt it and see the answer; only you see the answer.
A local model works when one party owns everything. But often you'll have more than one party, for example different machine operators, the machine manufacturer, the sensor provider, all involved, each with data they cannot or IP they do not want share. That’s where FHE becomes truly useful and more than an additional layer of protection by enabling collaborations that were simply not possible before.
It'll be very useful for even local setups when the data needs to be confidential. Look at the research related to medical training of llms (ignore their current lack of direct usefulness/trustworthiness, those are potentially solvable), with a homomorphically encrypted session, the large inference servers never see hipaa protected data in a way that exposes it if the server is compromised even if its an on premises setup in your doctors office processing the data, reducing risk of the data leaking anywhere.
Thanks a lot. Though encrypted AI chat bot is not far ahead, do not think of that as the primary target of today. Instead, there are many untapped applications (e.g. inter-bank transfers, industry, healthcare) where regulations, privacy laws and compliance requirements restrict institutions from touching data. These will initially be our key enablers, and over time we hope to extend the range of applications.
Have you considered that maybe the computation itself on the private data, no matter how you put it, even if encrypted, was designed to protect the consumer?
I.E., gaining any sort of insight a transaction of protected information is what the protections were in place for.
So is FHE more about skirting regulations and privacy laws? Or, is it a new frontier of an untapped data source that has some red tape around it?
To me, something was simply not encrypted properly if you are able to draw conclusions/learn insights/detect anything about the data. It's in conflict with the idea of what secure encryption means to me.
The server providing the FHE-based service does not gain any sort of insight. This is a key point: only the client can see the output of the computation.
this is only true in the IND-CPA model for most "practical" FHE work. So a more precise way to describe things is "a server who faithfully performs the task given to them does not gain any sort of insight" (perhaps with an additional caveat about decryption failures).
