Practically Solving LPN in High Noise Regimes Faster Using Neural Networks
This work addresses the LPN problem in cryptography, offering a practical speed-up for high-noise scenarios, though it is incremental as it builds on prior neural network approaches.
The authors tackled the learning parity with noise (LPN) problem by designing two-layer neural networks that outperform classical algorithms in high-noise, low-dimension regimes, achieving a speed-up from 3.12 days on 64 CPU cores to 66 minutes on 8 GPUs for dimension n=26 and noise rate τ=0.498.
We conduct a systematic study of solving the learning parity with noise problem (LPN) using neural networks. Our main contribution is designing families of two-layer neural networks that practically outperform classical algorithms in high-noise, low-dimension regimes. We consider three settings where the numbers of LPN samples are abundant, very limited, and in between. In each setting we provide neural network models that solve LPN as fast as possible. For some settings we are also able to provide theories that explain the rationale of the design of our models. Comparing with the previous experiments of Esser, Kubler, and May (CRYPTO 2017), for dimension $n = 26$, noise rate $τ= 0.498$, the ''Guess-then-Gaussian-elimination'' algorithm takes 3.12 days on 64 CPU cores, whereas our neural network algorithm takes 66 minutes on 8 GPUs. Our algorithm can also be plugged into the hybrid algorithms for solving middle or large dimension LPN instances.