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  • Hacker News
  • A homomorphic encryption blogpost without any mention of the computational overhead...
    by Frannky
  • I've published two papers on using HE for ML and it's nice, but also alarming, to see big players like Google and Microsoft making decent tools for performing HE. Although the technology is still much farther out from being commercially viable, it does pose an interesting problem about how these data aggregation companies will utilise a tech that is inherently private.
    by sheesdev
  • I guess I'm curious from your perspective what and why makes the tech from being commercially viable, and the concerns of big players adopting this (which was always going to happen I imagine, but when shapes a lot of the future of the field).
    by tolugenius
  • One potentially simple answer is: to make things that are otherwise illegal (for regulatory reasons) possible.

    There are some instances of hand-built cryptographic protocols used in production at Google that use FHE in small doses (e.g., computing a single dot product as part of an interactive protocol), so the big question is whether having a general-purpose tool will expand the feasibility of applying these to other, maybe less critical projects that still want some privacy guarantees but can't afford to hire cryptographers.

    by j2kun
  • 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?

    by stephen_cagle
  • If the bottleneck is data transfer rate or volume, then I think you’ll find more use from today’s FHE. Incidentally there is a fair amount of overlap in the use case space between homomorphisms and oblivious transfer.
    by catlifeonmars
  • Has FHE really progressed so far that it's now so efficient that doing computation on an encrypted prompt is feasible? I thought even basic operations like FHE addition were still thousands of times more complex. The only mention in the article I see is:

    > But while homomorphic encryption has a nontrivial cost overhead, it shifts the capability/privacy trade-off to a question of cost. And the cost of homomorphic encryption is rapidly decreasing.

    Which doesn't spell out exactly hon "nontrivial" the cost overhead still is.

    by bmenrigh
  • There are LLM models which compute only using addition no?
    by sean_pedersen
  • It's not running LLMs.
    by nl
  • addition is easy/essentially the same cost as standard (not really, because you have to compute mod p addition rather than mod 2^32, but ignoroing that it's roughly the same).

    as a general rule multiplication is the difficult part.

    it's hard to accurately quantify what "nontrivial" cost overheads are because they're very application dependent. for example, things that require encrypted control flow are very hard under FHE. so an encrypted hashmap sounds roughly unimplemnetable. but things that do not require encrypted control flow (e.g. many ML applications) are less bad. this can still be quite bad though. for example, relu is trivial in plaintext. it is hard homomorphically, because the trivial way to write it uses private control flow.

    by mswphd
  • https://fhetextbook.github.io/ which i believe was linked on this website previous is what i used to learn about how this stuff works.
    by magios
  • Also this lecture by Vinod Vaikuntanathan is really helpful if you want to learn about FHE: https://www.youtube.com/watch?v=5LGwaICJ5sw
    by s20n
  • "hotword" detection, is this basically sniffing encrypted data for words the authorities don't want you to speak?

    Seems counterproductive to their stated aim of privacy.

    by HlessClaudesman
  • from what I understand, only the holder of the private key would be able to see if a hotword was detected or not. even if Google ran hotword detection over your data, they’d only get a encrypted output on if the hotword was detected or not
    by hyusap
  • Not even just by the authorities. This sounds like the wet dream of Google itself or any other data mining companies. You can just ship the most sensitive data around and extract signals to your hearts content - and even if it would be protected by law, you're good because hey, it's always encrypted, and the signals, ad assignments and profile markers are encrypted too!

    This is what I don't like about all this "I'm not touching you" privacy tech, also including the new trend of nonvoluntary on-device processing.

    They pretend "privacy" was a primary goal of it's own, completely detached from any reasons why you would want to have it.

    Then they go on gloriously holding up the flag of privacy, while at the same time enabling all the things that you wanted to have privacy against in the first place.

    by xg15
  • Google is making private AI practical with Gemma4 something that you can run without an Internet connection.

    All of the proofs of privacy rely on us getting the math right. All of the privacy from unplugging your internet cable is there by default.

    by lsb
  • This is the same Google that doesn't have e2ee on their password manager by default. Like WTF, it's a password manager.
    by Cider9986
  • what would e2ee for a password manager even be? This reads like "Whoa, this encryption key isn't encrypted, we should encrypt it for added security"
    by TZubiri
  • That doesn't protect their ad targeting in the eyes of regulators, so no point in turning that on by default
    by cantalopes
  • Former Googler here. E2EE is easy. Nobody gets promoted at Google for solving easy problems. In fact if you set out to solve an easy problem, it looks bad at performance review time.
    by oofbey
  • If you think about it for a moment you will realize the average user does not want that.
    by amazingamazing
  • I think that, if you are under the impression that having an administrative back door is an unpopular feature, then you should speak to more of your non-technical friends about their experience of technology. I think you'll find that the people who could most benefit from a password manager often don't have good credential management and would be worse off if they lost all their passwords when they forgot or lost their master password.
    by aeturnum
  • F. Scott Fitzgerald's test of top-tier intelligence -

    > Holding two opposing views in the mind means accepting two contradictory ideas at the same time without needing to pick one side or rush to a simple answer

    I continue to use Apple products because they are top class even though everytime I think of Tim Cook in the Oval Office presenting the gold plaque to the current president, it makes me wanna puke. World isnt black or white.

    by bitpush
  • I think you folks are reading too much into it. I think the people working on FHE need to publish an AI-oriented pitch to retain funding from AI-pilled execs. Must be the same case with the golang post few days ago.
    by never_inline
  • Yeah I don't think they actually want this. There's far too much deeply personal and useful data being freely feed to the companies running these models.

    People are giving them everything. Their thoughts, feelings, intentions, company IP... It's a fucking gold mine.

    There's just too much value in retraining on that data and even if there wasn't, the feds lean so heavily on Google search histories to get convictions I am certain there will be pressure to prevent any mass market services from adopting this.

    by fedpost
  • So much inefficiency just to run it on someone else's untrusted hardware. Private AI is already possible today with local open-weight models running on hardware you control.

    Homomorphic encryption is cool technology, but I'm really not sure what problem it solves.

    by snovv_crash
  • It's so that they can sell more things to enterprise customers
    by firtoz
  • There are many challenges we need to solve for privacy preserving compute. Legislations, legal matters, key management, HSM like devices that has limited compute capabilities. At Belfort, we are accelerating FHE, besides exploring such uses cases for FHE. Like this one with Google, we also have publishes use cases; https://belfortlabs.com/blog/encrypted-fraud-detection-with-... https://belfortlabs.com/blog/belfort-partners-with-lg-on-enc...
    by furkanturan
  • I bet this would've been ground breaking if this was an announcement from Apple though.
    by neya
  • Great, private AI, at the cost of >1000x the resource usage. Because apparently AI companies weren't already using quite enough energy to cook the planet.

    The most private AI is the one running on my own hardware, not in some giant data center.

    by meindnoch