I heard that homomorphic encryption can actually preserve all the operations in neural networks, since they are differentiable. Is this true? What is the slowdown in practice?
This is true in principle, yes. In practice, the way this usually works is by converting inputs to bits and bytes, and then computing the result as a digital circuit (AND, OR, XOR).
Doing this encrypted is very slow: without hardware acceleration or special tricks, running the circuit is 1 million times slower than unencrypted, or about 1ms for a single gate. (https://www.jeremykun.com/2024/05/04/fhe-overview/)
When you think about all the individual logic gates involved in just a matrix multiplication, and scale it up to a diffusion model or large transformer, it gets infeasible very quickly.
This is true in principle, yes. In practice, the way this usually works is by converting inputs to bits and bytes, and then computing the result as a digital circuit (AND, OR, XOR).
Doing this encrypted is very slow: without hardware acceleration or special tricks, running the circuit is 1 million times slower than unencrypted, or about 1ms for a single gate. (https://www.jeremykun.com/2024/05/04/fhe-overview/)
When you think about all the individual logic gates involved in just a matrix multiplication, and scale it up to a diffusion model or large transformer, it gets infeasible very quickly.