[rhindi]

It's really great to see more big companies getting into this game, ease of adoption is really the key here.

When it comes to FHE, there are 3 underlying paradigms you can target with compilers:

1. boolean circuits, where you represent your program as encrypted boolean gates. The advantage is that it's as generic as it gets, the drawback is that it's slow. TFHE is great for that, and it's what is shown here.

2. arithmetic circuits, where you represent your program as a combination of encrypted additions and multiplications. This goes much faster, but you are quickly limited in terms of usecases because you can only do a certain number of arithmetic operations. CKKS/SEAL targets that: https://www.microsoft.com/en-us/research/project/microsoft-s...

3. functional circuits, where you represent your program as a combination of homomorphic functions. Advantage is that you can do very complex things like deep neural network, the drawback being that you have limitations of the bits of precision for the computations. Concrete targets that: https://zama.ai/concrete/

[ganafagol]

These are all the same paradigm, in priciple. 2 is a special case of 3 and 1 is a special case of 2. All circuits differing in how powerful the processing nodes are.

[rhindi]

Yes, in practice however it makes a difference. Consider for example computing the ReLu function for a neural network:

- With boolean circuits you need to run dozens of boolean gates, which means a lot of underlying crypto ops. Works but expensive.

- with arithmetic circuits, you would approximate it using polynomials. Works but not with high precision.

- with functional circuits, you encore the function as a single “bootstrapping” operation. Works in a single crypto op.

Performance / precision tradeoffs will be very different in these 3 cases

[swiley]

The probably like it because it will let them run whatever they want on end user devices without having to deal with interference from the user.