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Google’s fully homomorphic encryption compiler – a primer

jeremykun.com

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Re: Google’s fully homomorphic encryption compiler – a primer

#121
post #106
post #90

Earlier quoted context omitted.

You don't need FHE for that. It's possible through some zero-knowledge schemes, such as zk-SNARKS, which is implemented in and popularized by shielded transactions on Zcash.

You don't need zk-SNARKS for that. It's possible through some schemes, such as blind signatures, which have been successfully implemented many times but their usage for currency has (iirc) proved legally problematic.

For the uninitiated:

https://sceweb.sce.uhcl.edu/yang/teaching/csci5234WebSecurit...

Chaumian mints are gaining some popularity in the bitcoin world: https://fedimint.org/

Re: Google’s fully homomorphic encryption compiler – a primer

#123

Earlier quoted context omitted.

That's not the only trust model at play. Here is a different example of where FHE becomes very useful. Cancer researchers will benefit greatly from patient data sets that might include very privacy centric elements such as genome sets, past medical history (of both them and relatives to help understand heredity aspects of the disease). Most people making an informed decision may be uncomfortable with this information…

Are "privacy" issues are more important for people than "dying from cancer" issues though?

It would be useful to have control data sets as well, from those without cancer/illness. This is a problem in a lot of medical research where those affected with an ailment are much more likely to share data, participate in trials, and donate their bodies to science than healthy individuals are.

Re: Google’s fully homomorphic encryption compiler – a primer

#124

Earlier quoted context omitted.

That's not the only trust model at play. Here is a different example of where FHE becomes very useful. Cancer researchers will benefit greatly from patient data sets that might include very privacy centric elements such as genome sets, past medical history (of both them and relatives to help understand heredity aspects of the disease). Most people making an informed decision may be uncomfortable with this information…

How exactly would FHE work here?

One example would be a project like Folding@Home but with much more sensitive/personal information.

Organizations/universities could compute on data provided by a custodian organization without having to care about data handling.

Re: Google’s fully homomorphic encryption compiler – a primer

#125

Earlier quoted context omitted.

What you are describing is basically a blind index (search for it, there are lots of good resources online). Blind indexes can be quite useful, but they have a number of limitations - the partial match issue as you point out, but also you cannot do range queries or sorting, and they leak some information (e.g. duplicates have the same index value). Blind indexes are most definitely not fully homomorphic encryption.

Right, but this service isn't advertising FHE.

That's false. From the FAQ on their homepage:

> Vaultree's proprietary encryption breakthroughs are in various encryption technologies traditionally limited to niche use cases. We finally enable users to process entirely encrypted data with Fully Homomorphic and Searchable Encryption (FHSE) and other technologies in the field. Explaining what they are would take all day, but here's a one-liner: FHSE enables data processing to be run directly on encrypted data in the same way as on plain text data.

Re: Google’s fully homomorphic encryption compiler – a primer

#127
post #99
post #98

Earlier quoted context omitted.

Put a few thousand more words to pad, and I’d read that book!

Yea collecting sci fi ideas is fun but writing a book is wayyy too hard sadly ...

There's an AI for that.

Re: Google’s fully homomorphic encryption compiler – a primer

#128
> First, the subset of C++ supported by the compiler is rather small. As mentioned earlier, all data needs to have static sizes. This means, e.g., you can’t write a program that processes arbitrary images. Instead, you have to pick an upper bound on the image size, zero-pad the image appropriately before encrypting it, and then write the program to operate on that image size. In the same vein, the integer types you choose have nontrivial implications on performance. To see this, replace the int type in the 32-bit adder with a char and inspect the resulting circuit.

> Similarly, loops need static bounds on their iteration count. Or, more precisely, xlscc needs to be able to fully unwrap every loop—which permits some forms of while loops and recursion that provably terminate. This can cause some problem if the input code has loops with complex exit criteria (i.e., break‘s guarded by if/else). It also requires you to think hard about how you write your loops, though future work will hopefully let the compiler do that thinking for you.

I'm wondering if Zig wouldn't be a more appropriate language here, given its extensive support for running code at compile time (which requires all involved variable values to be known) and its integer data types which come in any bit length (u1, u2, u3, …).

Re: Google’s fully homomorphic encryption compiler – a primer

#129

Earlier quoted context omitted.

That's not the only trust model at play. Here is a different example of where FHE becomes very useful. Cancer researchers will benefit greatly from patient data sets that might include very privacy centric elements such as genome sets, past medical history (of both them and relatives to help understand heredity aspects of the disease). Most people making an informed decision may be uncomfortable with this information…

Are "privacy" issues are more important for people than "dying from cancer" issues though?

Who is 'people'? They're not the same set, not all of cancer research is looking at people currently 'dying from cancer', nor are there going to be enough from different demographies declaring 'I am concerned about dying from cancer more than my privacy' and volunteering their data. And even if there were, that trait itself is probably skewing your data and not necessarily generalisable to people not (so particularly) concerned about 'dying from cancer'.

Re: Google’s fully homomorphic encryption compiler – a primer

#130

Earlier quoted context omitted.

That's not the only trust model at play. Here is a different example of where FHE becomes very useful. Cancer researchers will benefit greatly from patient data sets that might include very privacy centric elements such as genome sets, past medical history (of both them and relatives to help understand heredity aspects of the disease). Most people making an informed decision may be uncomfortable with this information…

Are "privacy" issues are more important for people than "dying from cancer" issues though?

Ugh.

All things equal, privacy is usually preferred. This technology would allow both. Why is it either or?

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